Influence of Delivery Persons on Customer Satisfaction in Online Commerce

Master's Thesis , 2025 , 103 Pages , Grade: 1,0

Communications - Public Relations, Advertising, Marketing, Social Media

At the time of parcel delivery, personal contact can arise between the customer and the parcel service employee. During this encounter, the person delivering the parcel can have a significant influence on the customer's perception. Through their appearance, empathy, and respectful handling of the parcels, they leave a positive impression on the customer. This master's thesis examines the influence of the parcel courier on customer satisfaction, loyalty, and customer word of mouth. With the help of the SERVQUAL model and its five dimensions “reliability”, “responsiveness”, “assurance”, “tangibility” and “empathy”, the perception of service quality is examined. An online questionnaire with closed questions was created to collect the data. A total of 112 people from Austria responded to the survey. The results of the study, which was conducted using SmartPLS, showed that the dimensions “responsiveness” and “tangibility” have a positive influence on satisfaction with parcel couriers. Furthermore, it was shown that customer satisfaction leads to loyalty and positive word of mouth recommendations. The study therefore also underlines the importance of long-term customer relationships for parcel services.

As the participants in this survey are mainly from Upper Austria and the sample size is limited, the results cannot be considered representative. Future studies could benefit from a possible adaptation of the questions of the SERVQUAL model. As practical advice for managers of parcel services, we can point out the importance of customer-friendly behavior and the corresponding training of employees.

Table of content

List of figures

List of tables

List of abbreviations

1. Introduction
1.1. Problem definition
1.2. Theoretical and practical relevance
1.2.1. Theoretical relevance
1.2.2. Practical relevance
1.3. Research gap, objectives, and research question
1.4. Structure of the thesis

2. Literature review and theoretical underpinning
2.1. Basic terms and definitions
2.1.1. Service quality
2.1.2. Logistics service quality
2.1.3. Customer satisfaction
2.2. Concepts and theories
2.2.1. Boundary spanning
2.2.2. Customer’s quality expectations
2.2.3. Customer’s quality perception
2.2.4. Characteristics of Salespeople
2.3. Status quo of the literature
2.3.1. Characteristics of delivery staff
2.3.2. Criticism of the model
2.3.3. Relationship between service quality and customer satisfaction
2.3.4. The role of trust and commitment in customer loyalty and satisfaction
2.3.5. Word of mouth
2.4. Hypotheses and research Model

3. Methodology
3.1. Quantitative research method
3.2. Research setting
3.3. Selecting the sample and data collection
3.4. Data analysis procedures
3.5. Ethical consideration

4. Results
4.1. Sample description
4.2. Descriptive statistics
4.3. Statistical testing
4.3.1. Reliability and validity of the model
4.3.2. Hypothesis test and predictive relevance of the model
4.3.3. Model test with control variables

5. Conclusion
5.1. Theoretical and practical implications
5.1.1. Theoretical implications and discussion
5.1.2. Practical implications
5.2. Limitations and future research

List of references

Appendix

Preface and acknowledgements

Due to my physical impairment, the text of this Master's thesis was created with the help of speech recognition software. The text was dictated and transcribed in German and subsequently translated into English.

I would like to sincerely thank Prof. Dr. Christoph Teller and Ms. Teresa Schwendtner MSc for their ongoing support. I would also like to thank my personal assistants Daniela, Katrin, Kerstin, Bianca, Viktor and my parents for their support during the courses.

Linz, in February 2025

Abstract

At the time of parcel delivery, personal contact can arise between the customer and the parcel service employee. During this encounter, the person delivering the parcel can have a significant influence on the customer's perception. Through their appearance, empathy, and respectful handling of the parcels, they leave a positive impression on the customer. This master's thesis examines the influence of the parcel courier on customer satisfaction, loyalty, and customer word of mouth. With the help of the SERVQUAL model and its five dimensions “reliability”, “responsiveness”, “assurance”, “tangibility” and “empathy”, the perception of service quality is examined. An online questionnaire with closed questions was created to collect the data. A total of 112 people from Austria responded to the survey. The results of the study, which was conducted using SmartPLS, showed that the dimensions “responsiveness” and “tangibility” have a positive influence on satisfaction with parcel couriers. Furthermore, it was shown that customer satisfaction leads to loyalty and positive word of mouth recommendations. The study therefore also underlines the importance of long-term customer relationships for parcel services.

As the participants in this survey are mainly from Upper Austria and the sample size is limited, the results cannot be considered representative. Future studies could benefit from a possible adaptation of the questions of the SERVQUAL model. As practical advice for managers of parcel services, we can point out the importance of customer-friendly behavior and the corresponding training of employees.

Keywords: SERVQUAL, customer satisfaction, parcel delivery, courier service, loyalty, trust, word of mouth.

List of figures

Figure 1: Structure of the thesis based on Saunders et al., (2019, n.p.)

Figure 2: Model of service quality based on Gronroos (1984, p. 39)

Figure 3: The gap model of service quality based on Li et al. (2011, p. 1)

Figure 4: SERVQUAL-dimensions based on Nautwima & Asa (2022, p. 32)

Figure 5: Conceptual model, own illustration

Figure 6: Result LimeSurvey

Figure 7: Construct of the edited research model

Figure 8: Structural model with outer loadings, path coefficient and Cronbach’s Alpha (n=112)

Figure 9: Structural model with B-values ant t-values (n=112)

Figure 10: Control Variables to CS with the B-values and t-values (n=112)

List of tables

Table 1: List of abbreviations

Table 2: Systematic analysis of the key literature

Table 3: Measurement scales

Table 4: Methodological table

Table 5: Statistics of survey participants

Table 6: Frequencies of gender (n=112)

Table 7: Frequencies of age (n=112)

Table 8: Frequencies of main residence (n=112)

Table 9: Frequencies of monthly income (n=112)

Table 10: Descriptive statistics of SERVQUAL (n=112)

Table 11: Descriptive statistics of customer satisfaction, word of mouth and loyalty (n=112)

Table 12: Outer loadings (n=112)

Table 13: Variance inflation factor VIF (n=112)

Table 14: Cronbach's Alpha and Composite Reliability (n=112)

Table 15: Average Variance Extracted AVE (n=112)

Table 16: Discriminant Validity - Fornell and Larcker Criterion (n=112)

Table 17: Discriminant Validity - Cross loadings (n=112)

Table 18: Discriminant Validity - HTMT (n=112)

Table 19: Result Hypothesis - Test (n=112)

Table 20: R[2] values of the endogenous latent variables (n=112)

Table 21: Effect Size, F-Square (n=112)

Table 22: Stone-Geisser Q2 Values (n=112)

Table 23: Test Control Variables to CS (n=112)

Table 24: Test Control Variables to WOM (n=112)

Table 25: Test Control Variables to LOY (n=112)

List of abbreviations

Illustrations are not included in the reading sample

Table 1: List of abbreviations

1. Introduction

1.1. Problem definition

You receive an email that your ordered package will be delivered this afternoon. At the agreed time, the delivery man rings at the door and since you are at home, the courier makes sure that you are the recipient and hands over the package to you. With your signature you confirm the parcel has been delivered successfully. Along the way, he explains what you can do if you are not pleased with the goods. In response to the question of what would have happened if he had not met you personally, the delivery man explains that he would then have put the package in front of the door because you live in a rural area (Source: self-edited).

Until the 1980s, logistics was mainly considered as a link between production and consumption and thus as a cost factor. This led to a focus on the efficiency of technical processes. Since then, the ever-increasing popularity of online shopping and greater competition has led to logistics being seen as a way to differentiate from other companies in the market. Competitive advantages can come, among other things, from the influence of logistics on customer satisfaction. The first attempts to create standardized measurement of logistics service quality were conducted by Mentzer et al. (1999). They integrated both technical and functional aspects of logistics services into their scale (Mentzer et al., 1999, pp. 11-13; Sower et al., 2001, pp. 47-58; Wilkins et al., 2007, p. 846).

Stank et al. (1998) showed that logistics services exert a major influence on the customer satisfaction of existing customers and the acquisition of new customers. The quality of logistics services influences customer satisfaction and loyalty. This indicates that logistics can also be understood as a support function of marketing. In this context, it is frequently referred to as logistics service quality (LSQ), which should be optimized by both marketing and logistics experts (Benuisiene & Petukiene, 2012, pp. 65-67; Stank et al., 1998, pp. 77-78).

Nowadays, the quality of logistics service moves to the center of attention. One reason for this is the observation that customers not only buy products and services but also choose total offers including delivery. When deciding on an offer, information and taking the customer's personal needs into account also play a role. A stronger focus on the skills and customer orientation of employees in the logistics chain accompanies this development (Chen et al., 2011, p. 571). Employees of delivery services occupy a special role due to the increasing importance of online commerce. Often, parcel delivery staff are the only people with whom the customer interacts directly in the purchasing process. Therefore, employees of delivery services greatly influence the evaluation of the products and services they deliver. As a result, the behavior of employees during the delivery process has an impact on customer satisfaction (Keller, 2002, pp. 45-47).

The moment of parcel delivery involves face-to-face contact between the customer and the delivery service's employees and can therefore be considered a service situation (Bettencourt & Brown, 1997, pp. 41-42). During the encounter, the deliverer of the package can have a significant impact on the customer's perception. Through his competent appearance, empathy, cheerful outlook, or by solving customer specific problems, value can be generated through interaction. A delivery service that has customer-focused employees has a higher probability of success and more satisfied customers. When a competent employee approaches customers, then they are less critical and more satisfied with the service (Alhelalat et al., 2017, pp. 47-48; Lusch et al., 2007, pp. 9-11; Mentzer et al., 1999, pp. 11-13).

Studies show that over 90 percent of the working hours of parcel delivery workers take place without direct supervision by their managers (Bowersox et al., 2000, p. 7). More importantly, employees of parcel delivery services act as bridge builders between the company and the customers. Thus, they can serve as knowledge brokers between the company's stakeholders and perceive the customers' needs. Because of their direct connection to the consumer, employees of delivery services have access to valuable information that can potentially increase customer satisfaction. Hardly any other employee of the company can enter the personal space of the customers in a similar way and interact with them. Through this interaction, a special customer relationship is established that allows an understanding of customer problems and information about them to be gathered and relayed to the company (Flint & Mentzer, 2000, p. 19). Besides the information function, employees of delivery services also represent the company whose products they transport to the customer. This involves, for example, making a good impression on the customer in the long term. One way to ensure this is to let the delivery people know what level of service is expected from them (Alexander et al., 2016, pp. 6034-6035).

The purpose of this Master’s thesis is to investigate the extent to which the parcel delivery person can influence satisfaction and repurchase intentions through his or her behavior when meeting the customer.

1.2. Theoretical and practical relevance

1.2.1. Theoretical relevance

The lockdowns forced many stores to switch exclusively to online sales (Fairlie & Fossen, 2022, p. 1863). This increased the relevance of delivery services. The study by Gulc (2021) shows that the quality of delivery services is an essential factor for customer satisfaction and loyalty. In other words, good cooperation with delivery services can be a competitive advantage in the current market environment and thus positively influence the long-term success of the company. At the same time, the growth in online shopping has also strengthened courier services in the long term (Gulc, 2021, pp. 3-5; Yu et al., 2013, pp. 26-27). The study by Yee & Daud (2011) focuses on the factors influencing customer satisfaction. In their research, they concluded that the dimensions "tangibility", "reliability", and "assurance" have a positive influence on customer satisfaction (Yee & Daud, 2011, pp. 7-8).

Libo-on (2021) concludes in his study that the dimensions “tangibility”, “reliability”, “assurance”, and “responsiveness” have an influence on customer satisfaction. Tang et al. (2022) report a significant correlation only for the dimensions “reliability”, “responsiveness”, and “empathy”. Siali et al. (2018) found that all five dimensions of the SERVQUAL model influence customer satisfaction (Libo-on, 2021, p. 62; Siali et al., 2018, p. 430; Tang et al., 2022, pp. 147-148).

As far as customer loyalty is concerned, Uzir et al. (2021) states that the quality of service has a significant influence on customer trust and commitment. The resulting emotional connection leads to higher customer loyalty. In their research, they conclude that special employee training has a positive impact on customer loyalty. Hamidin & Hendrayati (2022) also found that the service quality of parcel carriers has a positive impact on customer satisfaction and loyalty. Correa et al. (2021) also found this effect in their study. Lei et al. (2022) noted that delivery time commitment is a particularly crucial factor for customer satisfaction and loyalty (Correa et al., 2021, pp. 13-14; Hamidin & Hendrayati, 2022, p. 287; Lei et al., 2022, pp. 2-3; Uzir et al., 2021, pp. 7-10).

The theoretical relevance of the work stems from the factors that influence the quality of delivery and customer satisfaction. In this area, there have been relatively few studies to date that measure the quality of delivery services in general. This is certainly true for the Austrian market. In this Master’s thesis, the author attempts to fill this gap by identifying factors that influence the quality of the service. For this purpose, consumers have to be surveyed about their preferences and perceptions. The results may be used to improve the quality of delivery services over time (Gulc, 2020, pp. 140-142; Yu et al., 2013, pp. 26-27).

1.2.2. Practical relevance

In recent years, the importance of online shopping in Austria increased sharply. Due to the pandemic-related shutdowns, the retail sector in particular was severely affected. From 2019 to 2021, online retail sales increased by almost half, from USD 8.6 billion to USD 12.7 billion (Statista, 2022a, n.p.). Due to the pandemic-related store closures, people in Austria were forced to consider online stores. In times of the pandemic, we find ourselves in a changed situation where new opportunities for online stores arise. In general, more than 70 percent of the respondents claim to inform themselves on the Internet before buying a product. The most significant product categories in e-commerce in Austria are clothing, shoes, and physical purchases of books, movies, music, and games followed by household electronics (Statista, 2022b, n.p.).

A survey by Statista (2022c) revealed that direct home delivery is the most important advantage of online shopping for Austrian consumers. That two thirds of respondents consider delivery to be the principal factor illustrates the importance of service quality in the delivery process. From this, it can be deduced that online stores depend on the qualitative performance of the courier services to convince larger numbers of customers. This also implies that delivery services have to continuously work on improving the quality of their delivery processes (Statista, 2022c, n.p.).

According to Eger et al. (2021), fear leads to a change in purchasing behavior in times of increased risk of contagion. Fear of contagion represented the main cause of change in purchasing behavior during the pandemic. The initial restrictions also led to a change in people's lifestyles. Because of this, consumers experimented with new shopping options and were forced to adjust their habits. As for online shopping, it can be said that in the first phase of the pandemic, consumers focused on meeting basic needs and ordered larger quantities per delivery (Guthrie et al., 2021, pp. 2-3). In general, more emphasis was placed on products for well-being. In this regard, it is noticeable that online ordering was particularly frequent in April and May 2020. The number of orders declined again from June 2020 onward, as consumers became accustomed to the exceptional situation and the shops opened again (Eger et al., 2021, pp. 2-3; Pantano et al., 2020, pp. 210-211).

1.3. Research gap, objectives, and research question

Based on the information above, there are already some studies on the influence of logistics service quality on customer satisfaction and loyalty (Mentzer et al., 1999, pp. 11-13; Stank et al., 1998, pp. 77-78; Wilkins et al., 2007, p. 846). In addition, some studies are available that focus on the influences of face-to-face interactions of service personnel on customer behavior and satisfaction (Alhelalat et al., 2017, pp. 47-48; Bettencourt & Brown, 1997, pp. 41-42; Lusch et al., 2007, pp. 9-11).

However, there are hardly any studies about the factors that affect customer satisfaction on the last meters to the customer. One goal of this Master’s thesis is to find out how the delivery person can contribute to customer satisfaction in this situation. From this overarching goal, the following operationalized goals are derived, which can be achieved using an online questionnaire.

- Measure the influence of the dimension "reliability" on customer satisfaction (Caruana, 2002, pp. 814-816; Jun et al., 2004, pp. 821-824; Ngaliman & Suharto, 2019, pp. 86-88).
- Determine whether there is a link between the dimension "assurance" and satisfaction with the delivery service (Ho et al., 2012, pp. 114-115; Kant & Jaiswal, 2017, pp. 418-420; Lee et al., 2000, pp. 218-219).
- investigate whether there is a link between the “tangible aspects” of the delivery service and customer satisfaction (Ngaliman & Suharto, 2019, pp. 86-88; Shukri et al., 2020, pp. 1008­1009; Zineldin, 2005, pp. 330-333).
- Quantify the relationship between the “responsiveness” of delivery personnel and customer satisfaction (Al-Jazzazi & Sultan, pp. 2017, 276-278; Kuruuzum & Koksal, 2010, pp. 10-11; Shukri et al., 2020, pp. 1009-1010).
- Asses whether the emphatic behavior of employees of a courier service influences the satisfaction of customers (Blery et al., 2009, pp. 28-30; Khan & Fasih, 2014, p. 333; Lei et al., 2022, pp. 2-3).

The factors influencing customer satisfaction are examined with the help of the dimensions of the SERVQUAL model "reliability", "responsiveness", "assurance", "tangibility", and “empathy” (Ho et al., 2012, pp. 114-115). In addition, the intention is to investigate the influence of trust and commitment on customer satisfaction in logistics. From the objectives, the following research question can be derived.

RQ: What characteristics of delivery persons influence customer satisfaction?

1.4. Structure of the thesis

The thesis is divided into five parts. In the introduction, the problem is defined and the research question is derived accordingly. The literature section discusses the theoretical models that expand the understanding of customer satisfaction in the service sector. The SERVQUAL model forms the basis for understanding the dimensions of service quality that lead to customer satisfaction. The hypotheses which are evaluated in the empirical part of this thesis result from the literature research. The methodology section presents the online questionnaire developed, which is subsequently evaluated using SmartPLS. In the results section, the findings of the survey are presented and tested for reliability and validity. This is followed by the hypothesis test. In the conclusion section, the empirical results are compared with other researchers and practical guidelines, and recommendations for future research projects are derived.

Illustrations are not included in the reading sample

Figure 1: Structure of the thesis based on Saunders et al., (2019, n.p.)

2. Literature review and theoretical underpinning

2.1. Basic terms and definitions

2.1.1. Service quality

For decades, scientists have been studying various aspects that characterize the quality of services. In general, the literature distinguishes between the American approach and the Northern European approach to addressing the issue of service quality. According to the American school of Parasuraman et.al. (1985), quality is assessed by comparing the service provided and the service expected by customers (Parasuraman et al., 1985, pp. 42-43). By focusing on qualitative services, competitive advantages can arise when services are tailored to the needs of specific customer groups. Therefore, when developing services, it is of particular importance to know the customers' goals and problems. It should be remembered that in the case of services, it is not only the result that is significant but also the process with which products and services are conveyed to the customer. In practice, this means that a service-oriented company must work continuously on developing and improving processes. This process must involve employees who can assess the needs of customers well (Gronroos, 2007, p. 483). Kotler et al. (2002) defines quality as the totality of all characteristics of the product or service that satisfy customers (Kotler et al., 2002, p. 831).

Illustrations are not included in the reading sample

Figure 2: Model of service quality based on Gronroos (1984, p. 39)

The work of Gronroos (2001), who represents the Northern European school of thought, provides a further development of the above considerations. In his research, service quality is measured by comparing clients' experiences and expectations during the service experience. He distinguishes two quality dimensions in services. The technical dimension refers to the outcome and the benefits that the services have for the customers. Functional aspect refers to how you deliver performance. The quality of services also influences the image of a company (Endeshaw, 2019, pp. 88-91; Gronroos, 2001, pp. 150-151).

2.1.2. Logistics service quality

Logistics services have a major impact on customer satisfaction as they connect suppliers and customers. Companies that are aware of the importance of coordination in relation to customer behavior can improve their services and thus differentiate themselves from competitors (Kolodizieva et al., 2022, pp. 554-555; Querin & Gobl, 2017, pp. 91-95). Logistics services can be understood as goal-oriented activities aimed at meeting the needs of customers. The goal behind this is that customers are satisfied to the highest degree and recommend the company to others. To ensure this, it is important to consider two aspects. The physical aspects of the logistics service refer, for example, to the error-free delivery of the ordered goods in the right quantity at the right time. The second category of dimensions represents the interface between marketing and logistics and refers to communication, adherence to schedules, or quality of personal contact between the employees of the parcel service and the customers (Wang & Hu, 2016, pp. 895-896).

Logistics service quality refers to activities and processes that aim to add value to the customer through logistics (Sutrisno et al., 2019, pp. 85-86). The goal is to meet the customer's needs and thus maximize customer value. The studies make a distinction between a subjective and objective approach to service quality in the logistics sector. If the parcel service provider takes the objective approach, it incorporates the perceptions of the customers. To this end, particular attention is paid to ensure compliance with the customer's time, location, and information requirements. Furthermore, all available activities, such as transportation, storage, inventory management, information management, and packaging, are tailored to customer needs. In the subjective approach, customer feelings are not included when measuring service quality. The subjective approach only includes the beliefs of the package service provider (Ghoumrassi & Tigu, 2017, pp. 293-295; Kulyk et al., 2017, pp. 207-208).

2.1.3. Customer satisfaction

Basically, there are two different ways to approach the concept of customer satisfaction (Jamaluddin & Ruswanti, 2017, pp. 24-25). Customer satisfaction as a process describes the extent to which a product or service can meet the needs of customers. This involves comparing the actual characteristics of a product with standardized expectations. The required information can be obtained through surveys or evaluations. Based on the knowledge gained, products or services are tailored to the needs of customers (Simanjuntak et al., 2020, pp. 3-5). However, customer satisfaction can also be understood as an emotional response to the experience of products or services. From this perspective, satisfaction is viewed as a cognitive state associated with the fulfillment of needs (Rigopoulou et al., 2008, pp. 983-984).

From another perspective, customer satisfaction can enhance the perceived benefits that result from the purchase of products or services (Giese & Cote, 2000, pp. 1-3). Chen et al.'s (2007) definition is special in that the purchase process is also included in the considerations. The focus on processes is particularly significant in the case of service since the service can never be provided in exactly the same way. Therefore, the process perspective is relevant along with the functional and the technical perspectives of service quality. It is also vital to remember that cognitive and affective responses to products or services must always be considered in the context of time. For example, it matters whether the feeling of satisfaction occurs immediately after purchase or after consumption (Chen et al., 2007, p. 52; Yee et al., 2013, pp. 460-461).

2.2. Concepts and theories

2.2.1. Boundary spanning

The above explanations on the subject of customer satisfaction have shown that satisfied customers are the basis for the long-term success of the company. Often, those employees who interact directly with the client have a major impact on customer satisfaction. They are crucial in building long-term customer loyalty (Hocutt & Stone, 1998, pp. 118-119). Companies that understand that in the service sector the behavior of employees is of central importance treat them accordingly and will be able to survive successfully in the market. Thus, they play a significant role in ensuring the quality of the service. The reason for the employee's essential role is that they play a key role in creating a pleasant atmosphere. They also build a personal relationship with the customer through the service, which can leave a lasting impression on them. Employees also represent the company they work for and the products they use or bring to customers. As a result, they can contribute decisively to customer satisfaction at the moment of service delivery (Bitner et al., 2000, pp. 142-144; Kumar & Pansari, 2014, pp. 54-56; Rowley, 2005, pp. 577-579).

Vargo & Lusch (2014) stated that in the service sector employees act as bridge builders between the company and the external as well as the internal environment. Service employees are the interfaces between management and customers in the service sector. By interacting with the customer and their feedback, they can collect, filter, and share essential information that helps the company to further develop its products or services (Barrett et al., 2015, pp. 140-141). Service employees with whom the customer is satisfied enjoy an elevated level of trust among customers. As a result, they can also contribute to solving problems on an interpersonal level. Due to their interface function, they are exposed to higher stress every business day, as they have to perform emotional work permanently. In the service sector, the emotions with which employees encounter customers play a significant role. It is the task of the staff to fulfill the wishes and expectations of the customers and to harmonize them with the requirements of colleagues, superiors, and customers. They are also confronted with the conflict of quality versus efficiency, which has a major impact on delivery staff in particular. Delivery workers often face a large volume of packages to hand out each day, and the quality of service suffers as a result (Ostrom et al., 2015, pp. 134­135; Vargo & Lusch, 2014, pp. 5-6; Wilder et al., 2014, pp. 448-449).

2.2.2. Customer’s quality expectations

According to Adat et al. (2014), customers' expectations are formed based on beliefs about quality standards for products or services. The basis for this is a person's previous experience, wishes, and needs. In addition, the personal needs of buyers, promises of the staff, or advertising messages influence the expectation of how the service should be provided. Furthermore, expectations are significantly influenced by the price of the product or service mentioned in commercials. Moreover, people's expectations are also formed through contact with family members, friends, experts, or other customers of the company. If expectations are not met, customers are dissatisfied and therefore change suppliers. Customers react very emotionally when expectations are exceeded or not met. Problematically, expectations grow after every positive experience (Ho et al., 2003, pp. 67-69). Another problem is that expectations can be affected by changes in price, offer, or distribution policy. The challenge that arises is that, on the one hand, companies have to set high standards to be competitive in the market, but on the other hand, they have to temper the expectations of some demanding customers in order not to disappoint them. In any case, companies must fulfill those promises that they communicate through their various channels (Abedin et al., 2016, p. 112; Adat et al., 2014, p. 2650).

Regarding the delivery of physical products, the issue is to truthfully communicate the functionality of the products and the expected delivery time (Fida et al., 2020, pp. 2-3). Consumers expect fast and reliable delivery of the items ordered. Any delays and additional costs associated with delivery should also be communicated transparently straight away. Information concerning delivery must be communicated on various channels to ensure that all customers are informed. Caution must be adopted to address concerns as individually as possible and to find customer-friendly solutions. Another essential function is package tracking. Customers have already become used to being able to check the status of the delivery at any time. Recently, customers have been paying more attention to the careful handling of personal data (Parasuraman et al., 1994, pp. 111-114; Sumaedi et al., 2012, pp. 84-85).

2.2.3. Customer’s quality perception

Whether a customer is satisfied with a product or service is significantly related to his individual feeling of the functions. Essentially, it is important to understand how people react to a stimulus from the environment. The reaction depends primarily on a person's expectations, needs, and values (Almsalam, 2014, pp. 79-80). According to Sureshchandar et al. (2002), customer perception is influenced first of all through subtle signals that can be sent through advertising, brand image, or price. The perception of service quality is thus also determined, among other things, by the three factors just mentioned. The foundation for these considerations is provided by the "expectation disconfirmation theory". It states that consumers compare the actual performance of a product or service with their expectations. As a result of the comparison, positive, negative, or neutral impressions of companies can arise. The key point is that if you want customers to be satisfied with a product or service, you have to surprise them in a positive way. According to the theory, meeting their expectations without leaving an additional "wow effect" merely leads to neutral feelings. In summary, service quality perceptions are thus shaped primarily by subtle stimuli that we are not always consciously aware of but nonetheless significantly influence our perceptions (Rao & Sahu, 2013, p. 40; Sureshchandar et al., 2002, pp. 364-366).

In the previous section on perceptions of service quality, customer expectations and customer perceptions were linked to each other. It is the task of companies to ensure that customer expectations are met or, even better, exceeded. For this purpose, it is necessary to close any gaps that may arise between the expectations of the product or service and its actual characteristics (Dabholkar et al., 2000, pp. 165-167). In order to do this, however, the first step is to establish awareness of these gaps among employees and managers. Parasuraman et al. (1985) developed the gap model as a tool to help achieve this. It identifies five conceptual gaps in the service process that need to be closed in order to align the expectations and experiences of customers in the service process (Jain & Gupta, 2004, p. 27; Parasuraman et al., 1985, pp. 43­46).

The first step is to analyze customer expectations. According to Jain & Gupta (2004), this can be done through surveys or feedback rounds in which they talk about their needs. Based on the insights gained, processes are then reconsidered and changed. One way of doing this is through employee training that is specifically tailored to weaknesses. However, customers' expectations may not be fully met. In this case, the company must adapt its communication with customers and ensure that they know what to expect from products or services. In this way, the company can ensure that they are satisfied. The knowledge gained from the gap model can be used to continuously improve products and services, thus contributing to customer loyalty (Dehghan et al., 2011, pp. 4-5; Jain & Gupta, 2004, pp. 27-28; Mokhtary & Pazhouh, 2012, p. 640).

The first gap can occur when the actual expectations of customers do not match the assumed expectations of management. Focusing on the delivery services industry, management might think that delivery within two days is satisfactory to the customer. In fact, customers might expect next­day delivery. The gap is that the delivery time is too long from the customers' perspective (Franceschini & Mastrogiacomo, 2018, pp. 88-90; Parasuraman et al., 1994, pp. 111-115).

The second gap can arise when management's perception does not match the guidelines defined by management. Managers may assume that delivery is fast enough. In reality, however, management's quality standards regarding delivery time are not met by the delivery service. One reason could be that there are too many packages to deliver per day (Dehghan et al., 2011, pp. 4-5; Li et al., 2011, p. 1).

The third gap appears when the company cannot meet the quality standards set by the management. It could be that the average delivery time is higher than the one set in the quality standards. One solution to solve this problem could be to increase the efficiency of the processes (Ahn et al., 2009, p. 214; Hussain et al., 2012, pp. 38-39; Zeithaml et al., 1988, pp. 35-36).

The fourth gap results from the difference between previously promised and actual delivery time. For example, the company may offer to deliver the package within one day. However, if it takes an average of two days for the package to reach the customer, the parcel service is perceived as unreliable (Ryglova, 2011, p. 259; Zeithaml et al., 1988, pp. 35-36).

In the fifth gap, the actual service is once again compared with the expected service. If the delivery time is too long, the customer is dissatisfied with the service and will therefore choose a different supplier next time. To decrease the fifth gap, it is necessary to take care of the other gaps first (Ahn et al., 2009, pp. 214-215; Gilmore & Carson, 1996, p. 40).

Illustrations are not included in the reading sample

Figure 3: The gap model of service quality based on Li et al. (2011, p. 1)

2.2.4. Characteristics of Salespeople

Basir et al. (2010) states that salesmanship refers to the ability to entice customers to buy. In addition, the term encompasses the independent conduct of sales conversations and the associated application of sales strategies, such as adaptability, the art of negotiation, asking questions and communication skills. These skills can also be referred to as empathy, which illustrates the link between salespeople and customer-focused delivery people (Basir et al., 2010, pp. 53-54; Churchill Jr. et al., 1985, p. 113; Williams & Spiro, 1985, pp. 435-436).

Situational sales behavior

This approach describes the ability of a salesperson or an employee of a courier service to adapt their communication with the customer to the circumstances at hand (Terho et al., 2012, p. 176). The prerequisite for this strategy is the collection of customer data at all stages and in all situations of the buying or delivery process. The more details available about a customer, the better the performance of the salesperson. The challenge for the company is to provide the representative with relevant information about the customer at the right moment. This ensures that relevant information and suggestions based on the customer's preferences can be passed on during the sales conversation. This tactic works best if the sales or delivery person is able to correctly interpret the customer's emotional reactions and respond to them adequately. According to Basir et al. (2010), it is particularly crucial to pay attention to gestures such as smiles or nods from the customer. Sales or delivery staff must therefore always pay attention to communication in order to be successful in their job. In summary, non-verbal communication can therefore provide clues about the outcome of the conversation (Basir et al., 2010, pp. 53-54; Zoltners et al., 2008, pp. 116­118).

Spiro & Weitz (1990) view every interaction with customers as a learning process that leads to satisfactory results from the customer's point of view in different situations. It is therefore of little surprise that good salespeople are characterized by an above-average willingness to learn. They have exceptional knowledge of the market, tend to be curious, do not shy away from challenges, and are always in search of new opportunities (Franke & Park, 2006, pp. 694-695; Spiro & Weitz, 1990, pp. 62-63).

Another prerequisite for the success of adaptive strategies is that frontline employees receive support from the entire company (Keillor et al., 1999, pp. 102-104). As mentioned earlier, employees who interact directly with customers can function as bridge builders between the company and the environment, making them invaluable to companies. The information gathered can be used to tailor products and services to meet customers' needs (Darmon, 2008, p. 228; Sujan, 1999, p. 26).

Active listening

A common feature of parcel carriers and sales employees is that they are able to recognize and interpret verbal and nonverbal signals from customers in their everyday work and adapt their strategies accordingly. According to Basir et al. (2010), frontline employees also build long-term relationships with customers that are central to the company's success by actively listening. For this reason, it is critical to pay attention to details during interactions with customers so that they feel that there is interest in their concerns. The result of such a relationship is mutual trust and the feeling of being well taken care of. Asking the right questions at the right time reinforces the feeling of trust, creating a comfortable atmosphere (Basir et al., 2010, pp. 54-55; DiGaetani, 1980, p. 42; Keillor et al., 1999, p. 101).

An intensive exchange of information is just as important for the development of trust as the disclosure of all relevant information and a cooperative attitude of all parties involved. To build trust, companies must keep all communication channels open for customers to receive information regularly (Maqbool et al., 2020, pp. 206-207). In addition, salespeople as well as delivery personnel who are available for the customer in case of questions and problems demonstrate commitment. In terms of information exchange, both private and professional topics can be addressed. Cooperative behavior that builds trust refers to actions that help the customer solve a problem. In general, it is important in relationships to always focus on positive interactions built on mutual respect (Amyx et al., 2016, pp. 587-588; Castleberry & Shepherd, 1993, pp. 35-36).

2.3. Status quo of the literature

2.3.1. Characteristics of delivery staff

Developed by Parasuraman et al. in 1985, the SERVQUAL model is widely used for service quality measurement. The model consists of a comparison between perception and expectation of service quality. The goal is to reveal strengths and weaknesses of the service. There are different versions of the model in the literature. Basically, it consists of ten components that have been grouped into four or five categories. The dimensions used to measure service quality are listed below (Nautwima & Asa, 2022, pp. 32-33; Parasuraman et al., 1985, pp. 41-50).

Illustrations are not included in the reading sample

Figure 4: SERVQUAL-dimensions based on Nautwima & Asa (2022, p. 32)

Tangibility

Ngaliman & Suharto (2019) states that tangibility refers to those characteristics of services that can be experienced without having used the service. This primarily includes the visible components of the service, such as equipment, personnel, premises, and appearance. According to Shukri et al. (2020), the better the tangibles provided by the organization, the superior the service is to the customer. This dimension is significant because it also influences the customer perception of the reliability of the organization. In addition, it also affects the customer's perceived competence and credibility of the company. Customer perception of the visible aspects can be improved by better branding the delivery vehicles and using more modern equipment. The external appearance of the delivery person can be improved with the help of a professional suit. A neat appearance should also be as natural as treating the customer with respect. It can also be noted that customer satisfaction increases the more positive experience the customer has with the delivery service. This dimension is especially important in industries such as restaurants and courier services, where direct interaction with the customer plays an important role (Ngaliman & Suharto, 2019, pp. 86-88; Shukri et al., 2020, pp. 1008-1009; Zineldin, 2005, pp. 330-333).

Reliability

Reliability can be defined as the competence of employees to take responsibility for ensuring that services are delivered according to the existing agreement. Ngaliman & Suharto (2019) stated that reliability is one of the most important aspects of service quality. Companies must deliver on their promises to ensure that they are trusted by their customers. In terms of package delivery service, reliability refers to delivering the package on time and at the agreed location (Caruana, 2002, pp. 814-816; Jun et al., 2004, pp. 821-824; Ngaliman & Suharto, 2019, pp. 86-88).

The promised service must conform to the agreed terms regarding availability, price, and problem resolution. In many studies, this dimension has been shown to have the greatest impact on customer satisfaction (Sumi & Kabir, 2021, pp. 5-6).

Ali et al. (2021) stated that increasing reliability helps a company to gain a good reputation, which leads to higher sales. Customer satisfaction can be increased if a company provides a reliable service. According to the researchers, satisfaction and trust are created when the company can offer a guarantee of efficiency in processing and meeting the delivery date. Gulc (2020) stated that reliability plays an important role in satisfaction with courier services, as it represents an essential point of contact with the customer. Consistency of delivery information, such as the correct delivery address, the correct recipient, and careful handling of the package, is particularly important in this regard. The parcel carrier is responsible for carefully securing the parcel in the vehicle during transport and delivering it to the customer intact. He should also act as a contact person for the customer in case of problems. Furthermore, the courier service must offer additional services and be available to the customer in the event of trouble (Ali et al., 2021, pp. 68-69; Gulc, 2020, pp. 140-141; Ngaliman & Suharto, 2019, pp. 86-88).

Responsiveness

The responsiveness dimension is about giving customers the feeling that you are there for them and making an effort to respond quickly to their needs (Sumi & Kabir, 2021, pp. 5-6). This dimension consists of actively listening to customers and responding to complaints and problems. A key factor in this dimension is the waiting time for answers or support from the company. However, responsiveness can also be understood as the ability to adapt services to the customer's needs and to respond flexibly to changes in requirements (Ali et al., 2021, p. 66).

Al-Jazzazi & Sultan (2017) emphasized that employee responsiveness consists of letting customers know exactly when tasks will be completed, giving them undivided attention, recommending additional services, and responding to their requests. The delivery company should arrange individual delivery times with the customer. For courier services, the responsiveness dimension refers to the ability to organize the delivery process. In addition, delivery couriers are responsive when they assist customers with problems and provide a smooth service with appropriate punctuality. The willingness of employees to provide the desired service at any time has a positive impact on customer satisfaction and loyalty (Al-Jazzazi & Sultan, 2017, pp. 276-278; Sumi & Kabir, 2021, pp. 5-6).

According to Ali et al. (2021), responsiveness is the ability of service providers to help and offer appropriate services to customers. Responsiveness is primarily about how service companies interact with customers through their employees. Responding to individual needs, such as unpacking and shelving goods, increases customer satisfaction, as does the ability to resolve problems efficiently. The parcel carrier should inform the customer of any damage that may have occurred to the item. It would also be desirable to inspect the delivered goods for damage upon delivery and initiate the return process in case of obvious defects. (Ali et al., 2021, pp. 66-69; Kuruuzum & Koksal, 2010, pp. 10-11; Shukri et al., 2020, pp. 1009-1010).

Assurance

According to Sumi & Kabir (2021), the assurance dimension is about developing a climate of trust between customers, employees, and managers. This is achieved when the staff can use their knowledge and skills in the interests of the customer. This dimension is particularly significant in the banking, insurance, and medical sectors, where customers often perceive a high level of risk in connection with services. In such sectors, the outcome is particularly difficult for external parties to analyze. Therefore, it is of special importance for companies operating in these industries to build trust and loyalty between customers and employees (Ho et al., 2012, pp. 114-115; Sumi & Kabir, 2021, p. 5; Taran, 2022, pp. 1907-1908).

The assurance dimension also relates to the competence, knowledge, and courtesy of delivery staff, as well as the ability to build relationships with customers. To provide customers with security, delivery services must establish high quality standards. Parcel carriers must be regularly instructed on traffic regulations and trained in handling equipment. Careful treatment of parcels is also of foremost importance. When this is accomplished, customer satisfaction and loyalty increase (Kant & Jaiswal, 2017, pp. 418-420; Lee et al., 2000, pp. 218-219; Sumi & Kabir, 2021, pp. 5-6).

Empathy

Lei et al. (2022) states that empathy means paying individual attention to the customer, taking care of their needs and dealing with problems in a solution-oriented and professional manner.

People who are empathic also imagine how they would like to be treated in this situation and try to help the client in the best feasible way and therefore try to make the services accessible to as many users as possible. At its core, the goal is to make customers feel special. In practice, delivery staff can satisfy customers by responding to their special wishes. It is also about actively solving problems and offering delivery options that suit the situation. A customer-focused delivery person knows the people in their area and addresses them personally. Moreover, it is important to inform the buyer about delays in the delivery of parcels and to apologize if necessary. If a close relationship with the customer has been set up, the delivery person can take care of any problems that may have occurred during the delivery, thus demonstrating his customer orientation. It is important to always be friendly, respectful, and concerned about the customer's satisfaction (Blery et al., 2009, pp. 28-30; Khan & Fasih, 2014, p. 333; Lei et al., 2022, pp. 2-3).

2.3.2. Criticism of the model

In recent decades, SERVQUAL has proven as one of the leading methods for measuring the quality of services. Nevertheless, the model has been repeatedly criticized in the literature. One of the most important criticisms comes from Cronin Jr. & Taylor (1992), who argue that the comparison between perception and expectation is flawed. In their opinion, it is sufficient to study the perception of service quality, as they assume a high correlation between perception and expectation of service quality. Based on their in-depth analysis, they have developed the SERVPERF model. The distinctive feature of it is that service quality is measured only on the basis of customers' perceptions (Cronin Jr. & Taylor, 1992, pp. 63-64). Moreover, there are conceptual doubts about the measurement of consumers' expectations. According to Teas (1993), unlike consumer perceptions, expectations cannot be defined and measured so easily. Expectations are measured and interpreted differently by researchers, which justifies doubts about the concept (Teas, 1993, p. 31). Brown et al. (1993) suggest that the five dimensions of the service quality model may not be sufficient in some situations. In such a case, one must use industry­specific models. (Brown et al., 1993, pp. 134-136). In addition, they point out that the dimensions are interrelated, which may complicate measurement. Dabholkar et al. (2000) suggest that the SERVQUAL model may be more effective for companies with a high service ratio, such as hotels, than for industries focused on products (Dabholkar et al., 2000, pp. 165-167). In summary, despite its overwhelming acceptance over decades, the model still needs further development.

Despite the criticism, the model is very often used to measure customer satisfaction in the service industry. The reasons for this also justify the use in the present work. The SERVQUAL model evaluates the influence of service quality on customer satisfaction very extensively by five different dimensions. According to Sumi & Kabir (2021), this allows for a very comprehensive analysis of service quality in companies. The questionnaire used to collect the empirical data is a standardized, valid measurement instrument that has already been used in many different industries. Thus, the SERVQUAL model represents a proven method accepted in the scientific community. As already mentioned, the goal is to uncover gaps between perceptions of service quality and consumer expectations. The insights gained can be used to develop and prioritize improvement potentials as well as measures. This means that activities can be developed that are specifically tailored to the company's situation with the aim of improving customer satisfaction (Jones & Shandiz, 2015, pp. 51-52; Khorshidi et al., 2016, p. 198; Osman et al., 2020, pp. 159­161; Sumi & Kabir, 2021, pp. 5-6).

2.3.3. Relationship between service quality and customer satisfaction

According to Libo-on (2021) customer satisfaction can be understood as a feeling that occurs after the purchase of products or services. In this context, the customer is only truly satisfied if his or her expectations have been met or exceeded (Libo-on, 2021, pp. 55-56). When pursuing the traditional customer-centric approach to service quality, one aims to exceed the customer's expectations regarding the quality of the service. It is also about being considerate of customers' time requirements (Joyner Armstrong et al., 2018, p. 560). Furthermore, the transcendent view of service quality emphasizes that excellent service is necessary to satisfy customers and encourage them to purchase again (Viswanadham et al., 1996, p. 222).

Research results have shown that there is a high correlation between the quality of services and customer satisfaction. Siali et al. (2018) has demonstrated that an improvement in the quality of service leads to increased customer satisfaction. Customer satisfaction, in contrast to service quality, usually refers to individual interactions with companies. Service quality is mostly about perceptions that span multiple touchpoints with companies (Siali et al., 2018, p. 430).

As already mentioned, the "expectation disconfirmation theory" concludes that companies that meet customer needs or positively surprise them can expect a higher number of satisfied and loyal clients throughout the long term. In general, service quality and customer satisfaction are interrelated, as customer satisfaction is a result of positive evaluation of service quality (Libo-on, 2021, p. 62; Voon et al., 2009, pp. 34-37).

The following table presents the findings on the relationship between the SERVQUAL model and customer satisfaction in the courier service industry. The articles were selected based on the topics of customer satisfaction and customer loyalty/trust.

Table 2: Systematic analysis of the key literature

Illustrations are not included in the reading sample

As the table shows, all dimensions of the SERVQUAL framework influence customer satisfaction. However, it is noticeable that there are considerable differences between the results of the studies. They come to different conclusions regarding the strength of the influence of the different dimensions. According to the study by Gulc (2020), these can possibly be explained by the fact that preferences differ depending on the customer segment. In this regard, it might be important to conduct further studies that shed more light on the differences related to demographic factors. Moreover, the results could vary in different countries and cultures. Furthermore, minor changes in the questionnaire, for example in the choice of words or the order of the questions, could explain the differences. However, despite the divergence, all dimensions of the model can be expected to have an impact on customer satisfaction (Gulc, 2020, p. 148; Kashif et al., 2014, pp. 678-679; Raajpoot, 2004, pp. 189-199).

Despite the high correlation between customer satisfaction and service quality, there are significant differences between these two concepts (Yap & Kew, 2007, p. 62). For example, customer satisfaction can also arise from factors unrelated to service quality, such as customer needs or beliefs of company fairness. Also, unlike factors related to quality, customer satisfaction ratings require personal experience with the company. Perceptions of quality, in contrast, are often based on ideals, ideas, and desires. Nevertheless, the dimensions that influence quality are quite specific compared to those that influence customer satisfaction (Choi et al., 2004, p. 919). Although there are differences, studies from Kiran & Diljit (2011) have shown that there is a positive correlation between service quality and customer satisfaction. This suggests that high perceived service quality also leads to high customer satisfaction (Kiran & Diljit, 2011, pp. 108­109; Li et al., 2004, p. 182).

2.3.4. The role of trust and commitment in customer loyalty and satisfaction

Loyalty relationship

In today's business environment, companies are placing increasing emphasis on building long­term relationships between suppliers and customers (Knemeyer & Murphy, 2004, pp. 36-38). Relationship marketing involves building, enhancing, developing, and supporting customer relationships that enable an exchange at eye level. Relationship marketing is about improving the interactions between suppliers and customers and thus increasing customer satisfaction and loyalty. Because of the difficulties associated with standardizing service, relationships between companies and customers are particularly important in the service sector. In addition, services require a higher level of interaction. These interactions are significantly influenced by personal relationships between employees and customers (Parsons, 2002, pp. 5-6; Too et al., 2001, pp. 291-292).

In this context, it is also referred to as relationship satisfaction, which is understood by some authors as part of customer satisfaction (Geyskens et al., 1999, pp. 231-233). Relationship satisfaction, in turn, is understood as a concept consisting of the following three elements: "satisfaction with the service", "satisfaction with the organization" and "satisfaction with the company's employees". This establishes the link between customer satisfaction and loyalty, as it is the basis for understanding relationships. It is important to understand that loyalty is about a lifetime assessment of the entire relationship, not a transaction-specific assessment of satisfaction with the company's products or services (Fullerton & Taylor, 2002, pp. 126-127; Lages et al., 2005, pp. 1041-1042).

According to the theory of Morgan & Hunt (1994), trust and commitment play a special role in this context. With the help of these constructs, it is possible to improve the measurability of relationships between companies and customers. They see trust and commitment as the basis for the success of long-term relationships. Also, trust and commitment promote cooperation between contractual partners (Morgan & Hunt, 1994, pp. 23-24). Another key point is that the relationship should be beneficial to all parties in the long term. Thus, the focus must not be on extracting the maximum advantage for one party in the short term. Opportunistic behavior would immediately destroy the trust that has been built up, as business relationships always involve a high level of risk. To reduce the risk of failure, it is important to agree on clear rules and standards that all parties must adhere to right from the start. This leads to trust, commitment, and loyalty building up over time (Cropanzano & Mitchell, 2005, p. 875; Lawler & Thye, 1999, pp. 21-22).

Trust

Trust also plays a special role in relationship marketing, as it is central to long-term cooperation. It is important to build trust at the very beginning of a customer relationship, which provides the basis for collaboration (Anderson et al., 2017, pp. 945-946; Langfield-Smith, 2008, pp. 348-350). A basic level of trust reduces stress and increases the willingness to adapt, which in turn increases the likelihood of successful collaboration (Han et al., 2014, p. 2118). Trust can accordingly be defined as a person's conviction that the other party will not exploit his or her weaknesses. However, trust can also be a combination of credibility and perceived kindness of a company towards its customers. Credibility here refers to a feeling and the taking of risks associated with the business relationship. Benevolence refers to the fact that a company is interested in the well­being of its customers and therefore refrains from activities that would harm the customers. In addition, both business partners have rights and obligations that must be respected so that the risks of cooperation are as low as possible. Furthermore, to achieve the agreed goal, they must honor agreements, share information some of which is sensitive, and be honest with each other (Castaldo et al., 2010, pp. 662-663; Dyer & Chu, 2000, pp. 261-263; Tian et al., 2008, p. 348).

According to Palvia et al. (2010), in relationships, trust is also referred to as the social dimension of success, as it can significantly influence the outcome of a transaction. Companies that have trustworthy partners can increase their creativity by expanding their core competencies. Trust even helps reduce costs and increase the effectiveness and efficiency of business collaborations. One reason for the lower costs is that the presence of trust reduces the need for control. In addition, trust between partners influences customer satisfaction. Trust comes from the reputation and competencies of companies. The results that can be achieved through trust are higher cooperation, higher customer satisfaction, and higher loyalty (Mazzola & Perrone, 2013, pp. 258­259; Palvia et al., 2010, p. 238; Sambasivan et al., 2011, pp. 550-555).

Managers of parcel services can inspire confidence in customers, for example, by providing them with detailed information about the delivery process. GPS tracking can be used for this purpose, for example, in order to be informed about the shipping status of the package at all times. The advantage of this technology is also that timely information can be shared in case of delays (Hamidin & Hendrayati, 2022, pp. 287-289). Good customer service is equally central to building trust. On the one hand, it is important that customer service responds to problems as quickly as possible and can be reached through various channels; on the other hand, it is a matter of training the parcel carriers so that service on site also meets customer expectations (Yee & Daud, 2011, pp. 7-8). Certificates that guarantee compliance with certain standards are also good for building a trusting and long-term customer relationship. In addition, experiences of satisfied customers that can be shared on certain Internet portals increase the trust of new customers (Akram et al., 2022, pp. 134-135).

Commitment

Engagement involves an assessment of the prospects for success of a relationship and the corresponding cooperative behavior of the contractual partners. This means that both convictions and a stable relationship with the company are prerequisites for commitment (Morgan & Hunt, 1994, p. 23). Commitment is therefore usually the result of a relationship that performs well in the long term. This is expressed, among other things, by the fact that the long-term benefit of the cooperation is more important than the short-term maximization of the company's profit. In other words, commitment is demonstrated by both the company and the customer investing in the relationship over an extended period of time (Chou et al., 2015, pp. 34-35; Han et al., 2014, pp. 38-40).

In practice, three different types of commitment can be distinguished (Bansal et al., 2004, pp. 236-237).

- Affective commitment
- Calculative commitment
- Normative commitment

Affective commitment arises when both contracting parties are satisfied and bound together by positive feelings. As a result, there is a feeling of identification with the company, which in turn tends to make the price of the goods or services less important. The customer maintains the relationship with the company for emotional reasons (Bansal et al., 2004, pp. 236-237; Cater & Cater, 2010, pp. 1325-1326; Roxenhall & Andresen, 2012, p. 88).

Calculative commitment occurs when partners feel compelled to continue the relationship because of costs. Expenses increase when the partners end the relationship. Cater & Cater (2010) also talk about switching costs, which can occur when shifting to a different provider. As a result, a so- called "locked-in" effect occurs. In terms of courier services, this would be the case, for example, if a yearly subscription with an initial investment were to be offered. In summary, the operation of calculative commitment is based on negative incentives (Cater & Cater, 2010, pp. 1325-1326; Roxenhall & Andresen, 2012, pp. 88-89; Sharma et al., 2006, pp. 84-86).

In normative commitment, the relationship continues because of moral obligations. This is based on rules, customs, or social norms. In contrast to the calculative form, the rules are usually not contractual but implicitly agreed upon. In the course of such a relationship, it may be difficult to end it due to high social pressure (Bansal et al., 2004, pp. 236-237; Cater & Cater, 2010, pp. 1325­1326; Sharma et al., 2006, pp. 84-86).

Patterns of repetitive buying behavior

Quality, like customer satisfaction, is based on emotional reactions that can occur after the purchase of products or services. The emotional attachment to a brand is an important element of loyalty and distinguishes it from other forms of repetitive buying behavior (Karatepe, 2011, p. 282). Among other things, this observation is crucial when it comes to measuring loyalty. In marketing literature, we speak of commitment and trust in this regard. These concepts also refer to the emotional bond that can develop between companies and consumers. According to Hansen et al. (2003), trust and commitment between companies and customers are carried by feelings of emotional satisfaction. It is precisely this commitment, which is based on an emotional connection between consumers and the brand in question, which distinguishes loyalty from other forms of buying behavior. Consumers may appear to be loyal to a brand in certain situations. However, their true motives driving purchase behavior remain hidden (Bowen & Chen, 2001, pp. 215-216; Hansen et al., 2003, pp. 362-363; Tsoukatos & Rand, 2006, pp. 504-505).

Marketing literature distinguishes between several different forms of loyalty. However, it is important to keep in mind that for the long-term success of a company, distinct types of consumers need to be addressed. The segmentation of consumers in terms of their loyalty is fundamental for the design of loyalty programs, among other things (Ndubisi, 2007, pp. 99-100; Reichheld & Sasser, 1990, p. 105).

One of the most important customer groups for companies is those who buy and use certain products or services, such as delivery, continuously over a certain period. Companies must try to build an emotional bond with this type of customer as a way to increase the value of the company. This leads to higher lifetime sales and referrals to family and friends, which can further increase business value. They are also less price sensitive and less concerned about failures in products or services (Fullerton, 2014, p. 670; Loyyl & Kumar, 2018, pp. 5-6). Active loyalty refers to customers who recommend products or services particularly frequently. This customer group is very active on social media and engages in word-of-mouth advertising (Oraedu et al., 2021, p. 126).

In contrast, customers whose buying behavior is based on their attitude or their beliefs are less intensively connected with the company. They are more likely to buy a product or service for rational reasons, such as service quality or the company's reputation (Purwianti & Khoviati, 2021, p. 157). Hamidin & Hendrayati's (2022) study showed that for parcel services, both service quality and satisfaction influence loyalty (Hamidin & Hendrayati, 2022, p. 287). A study by Correa et al. (2021) found that the service quality of delivery services has a positive impact on customer trust and loyalty. In addition, the service experience has been shown to have a particular impact on service quality (Correa et al., 2021, pp. 13-14).

2.3.5. Word of mouth

Kotler & Armstrong (2006) describe word of mouth as a form of verbal, non-commercial communication that can be about products, services, or brands. Word of mouth usually originates from a relatively small group of people (Kotler & Armstrong, 2006, p. 408). In terms of the medium, this form of communication can be either traditional person-to-person or online (Timothy Coombs & Holladay, 2007, p. 309). When opinions are exchanged online, this is referred to as electronic word of mouth (eWOM). This differs in terms of data protection, for example, in that it is written and anonymous and can be read by anyone at any time. This also means that product reviews spread across the internet much more rapidly and are easily accessible to all interested parties (Hennig-Thurau et al., 2004, p. 39).

In terms of content, a distinction is made between positive and negative word of mouth. Due to perceived authenticity, this form of communication has a major influence on the perceived quality of a product or service (Al-Ja’afreh & Al-Adaileh, 2020, pp. 185-186).

It is important that the information passed on comes from a credible source. Information from trustworthy sources has a profound influence on consumer behavior, as it directly influences purchasing decisions (Sweeney & Swait, 2008, pp. 181-182). Practical examples include reviews of products or services which, if considered credible, have a major influence on consumers' purchase intentions. Information from friends, acquaintances or relatives is also rated as highly reliable (Siddiqui et al., 2021, p. 1012). This illustrates that group dynamic processes and the receptiveness of the recipient have a major influence on the effectiveness of word of mouth.

Much research demonstrates the connection between service quality, customer satisfaction, and positive word of mouth (Leisen Pollack, 2017, p. 514). It has also been shown that word of mouth has a positive impact on the credibility of a brand and positively influences consumers' purchase intentions. In addition, it is one of the most efficient tools when it comes to spreading positive information about products and services and thus supports a company's advertising activities (Khan et al., 2015, p. 271).

Negative word of mouth, on the other hand, can have disastrous consequences for companies. A study by Timothy Coombs & Holladay (2007) has shown that sales figures fall when a product or service has negative word of mouth. In this case, customers often decide in favor of another provider. In general, it has been shown that the influence of negative word of mouth on customers is greater than that of positive word of mouth. The reason for this is that negative statements lead to uncertainty. In summary, it can therefore be said that negative word of mouth influences customer behavior, brand perception, and company success (Sweeney et al., 2020, pp. 149-150; Timothy Coombs & Holladay, 2007, p. 309).

2.4. Hypotheses and research Model

As stated in chapter 2.3.3, there is a correlation between the service quality of courier services and customer satisfaction. The empirical part of this thesis aims to investigate this with the SERVQUAL framework.

Parasuraman et al. (1988) states that the comparison of service perception and expectation is the core of the model and is crucial for customer satisfaction. In this process, consumers compare the services they receive with predefined standards. The "expectation disconfirmation theory" by Oliver (1980) states that consumers are disappointed when the service provided does not meet their expectations. When a company succeeds in exceeding expectations, this leads to satisfaction. So, the idea that customer satisfaction can be measured by comparing perception and expectation in five dimensions is the basis for the model. It is important to understand that customers' perceptions of quality are always subjective, which makes it much more difficult to measure. The model has already been successfully applied in many industries. A large number of studies suggest that service quality influences customer satisfaction (Bitner, 1990, pp. 70-72; Oliver, 1980, pp. 466-467; Parasuraman et al., 1988, pp. 23-24; Wilson et al., 2016, pp. 106-107).

Nevertheless, throughout the years, the model has been criticized for comparing perception and expectation. Critics suggested that expectations are difficult to measure and can change over time. In addition, customers' feelings are dependent on mood and expectations, among other factors, which can lead to biased results (Brown et al., 1993, pp. 134-136; Cronin Jr. & Taylor, 1992, pp. 63-64; Teas, 1993, p. 31).

Most studies on satisfaction with parcel services have been carried out with the help of the SERVQUAL model. In the previous chapters, it became apparent that the model is appropriate for measuring the service quality of parcel services due to the multifaceted nature of the questions asked in five interlinked categories. Different variations of the model exist in literature. In principle, it is composed of ten components that have been summarized into five categories. Subsequently, the research model is depicted graphically and hypotheses are generated, which will be tested empirically in the next chapters. The factors influencing customer satisfaction are analyzed with the dimensions "tangibility", "reliability", "responsiveness", "assurance" and "empathy" of the SERVQUAL model (Ho et al., 2012, pp. 114-115; Libo-on, 2021, p. 62; Tang et al., 2022, pp. 147­148).

The following seven hypotheses describe the research model used in this thesis.

- Hypothesis H1: There is a positive relationship between the tangibility of the delivery person and customer satisfaction.
The tangibility dimension relates primarily to the visible components of the service. This refers to aspects such as equipment, staff, premises and appearance that can be experienced without using the service. According to Shukri et al. (2020), the physical facilities of the company influence the quality of the services. In addition, Shukri et al. (2020) found that physical facilities can also increase customers’ perceptions of reliability (Shukri et al., 2020, pp. 1008-1009; Zineldin, 2005, pp. 330-333).
The customer's perception of competence and credibility can also be positively influenced by the quality of the physical facilities. Research by Ngaliman & Suharto (2019) found that a repeated positive experience with the delivery person increases customer satisfaction (Ngaliman & Suharto, 2019, pp. 86-88). In their study, Mulyono & Fikri (2023) also refer to this dimension as the "serviecescape", by which they mean those visible components that positively influence a company's image. By focusing on the physical aspects and the impression that the delivery staff makes on the customer, the efficiency of the delivery company can be increased (Mulyono & Fikri, 2023, p. 3).
- Hypothesis H2: There is a positive relationship between the reliability of the delivery person and customer satisfaction.
This dimension is about the company keeping its promises. Reliability thus increases customer satisfaction and trust in the company. Ali et al. (2021) found that the ability of employees to perform according to agreements plays an essential role. Important aspects that determine satisfaction are way problems are solved, the price and the availability of the service. In addition, Ali et al. (2021) showed that reliable delivery companies enjoy a better reputation, which leads to higher sales. Many studies have shown that this dimension has the greatest influence on customer satisfaction (Ali et al., 2021, pp. 68-69; Jun et al., 2004, pp. 821-824; Sumi & Kabir, 2021, pp. 5-6).
- Hypothesis H3: There is a positive relationship between the assurance of the delivery person and customer satisfaction.
The development of a climate of trust between customers, employees and managers is at the heart of the assurance dimension. This is possible when employees put their knowledge and skills at the service of the customer. Therefore, companies in these industries need to build trust and loyalty between customers and employees (Ho et al., 2012, pp. 114-115; Sumi & Kabir, 2021, p. 5; Taran, 2022, p. 1908). To ensure that customers feel secure, delivery services must adhere to high quality standards (Kant & Jaiswal, 2017, pp. 418-420).
- Hypothesis H4: There is a positive relationship between the empathy of the delivery person and customer satisfaction.
Empathy means paying individual attention to the customer, taking care of their needs and dealing with their problems in a solution-oriented and professional manner. Empathetic people also imagine how they would like to be treated in this situation and try to help the customer as much as possible and therefore aim at making the services accessible to as many customers as possible. The most important thing is to make customers feel special. In practice, delivery service personnel can increase customer satisfaction by fulfilling specific customer requests. This includes actively solving problems and offering delivery options that meet the customer's needs. It is important to always be friendly, respectful and strive for customer satisfaction (Blery et al., 2009, pp. 28-30; Khan & Fasih, 2014, p. 333; Lei et al., 2022, pp. 2-3).
- Hypothesis H5: There is a positive relationship between the responsiveness of the delivery person and customer satisfaction.
Responsiveness means giving the customer the feeling that you are there for them and trying to meet their needs quickly. This dimension includes active listening and solving complaints and problems. The time spent waiting for answers or help from the company is important for this dimension. However, responsiveness can also be understood as the ability to adapt services to customers' needs and to respond flexibly to changes in requirements (Ali et al., 2021, p. 66; Sumi & Kabir, 2021, pp. 5-6).
Al-Jazzazi & Sultan (2017) point out that staff responsiveness is about informing customers exactly when their tasks will be completed, giving them full attention, recommending additional services to them, and responding to their requests (Al-Jazzazi & Sultan, 2017, pp. 276-278). According to Ali et al. (2021), responsiveness is the ability of service providers to help customers and offer them appropriate services. Responsiveness primarily refers to the way service providers interact with customers through their employees. Customer satisfaction is increased by responding to individual requests, such as unpacking and putting away goods, as well as the ability to solve problems quickly (Ali et al., 2021, pp. 66-69).
- Hypothesis H6: There is a positive relationship between customer satisfaction and attitudinal outcomes.
Satisfied customers are characterized, among other things, by the fact that they recommend the company's products or services to others and buy the company's products again. Studies have shown, for example, that positive word of mouth (WOM) can both increase the loyalty of existing customers and attract new customers to the product or service (Farooq et al., 2018, p. 172; Gulc, 2021, pp. 3-5; Jun et al., 2004, pp. 821-824; Uzir et al., 2021, pp. 7-10).
Additionally, Susilowati & Yasri (2019) found that customers who had a negative encounter with the company were more likely to report their experience than satisfied customers. This underlines the importance of WOM for the long-term prosperity of companies. The starting point for success is the quality of service, which can positively influence both WOM and the image of the service provider. The study also showed that WOM both increases customer satisfaction and leads to greater customer satisfaction (Susilowati & Yasri, 2019, p. 678; Wang, 2011, p. 256; Yasri & Engriani, 2018, pp. 354-355).
- Hypothesis H7: There is a positive relationship between customer satisfaction and intentional outcomes.
Loyalty describes the long-term emotional connection between customers and the company. With regard to parcel delivery staff, Uzir et al. (2021) refer in particular to education and training, which have a major influence on customer satisfaction and loyalty (Uzir et al., 2021, pp. 7-10). Moreover, Correa et al. (2021) identified an effect of the service quality of parcel delivery staff on customer loyalty (Correa et al., 2021, pp. 13-14). Lei et al. (2022) found that adherence to the delivery date also has a positive influence on loyalty (Lei et al., 2022, pp. 2-3). It can therefore be deduced from the above findings that customer satisfaction with delivery companies increases the intention of having parcels sent again.

Akram et al. (2022) empirically confirmed the influence of trust on customer satisfaction and loyalty (Akram et al., 2022, pp. 134-135). In addition, the study by Hamidin & Hendrayati (2022) also found a positive correlation between trust in delivery services and customer satisfaction and loyalty (Hamidin & Hendrayati, 2022, p. 287). The study by Sutrisno et al. (2019) was also able to prove that customer satisfaction has a positive influence on the loyalty towards parcel services (Sutrisno et al., 2019, p. 90).

The conceptional model which is the basis for the empirical part is depicted below.

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3. Methodology

The methodology section of this Master's thesis serves as a roadmap for the research process and thus ensures the validity and reliability of the results. The following sub-sections serve to justify the methods selected to answer the research questions. The research design, the design of the questionnaire, and the methodology used to collect the data are discussed in more detail.

3.1. Quantitative research method

Quantitative methods aim to investigate relationships between different variables (Saunders et al., 2019, pp. 193-194). Regarding the research question, the quantitative approach is suitable because findings from other studies and theories are already available and can be verified with the help of a structured online survey. The explanatory paradigm of empirical research is therefore suitable for explaining the cause-and-effect relationships between certain characteristics of parcel delivery staff and customer satisfaction. In contrast to qualitative interviews, only closed questions are asked, which allows for many participants (Esteban-Bravo & Vidal-Sanz, 2021, pp. 24-25). The data obtained is then analyzed using statistical tools. Due to the design of the questionnaire and the statistical analysis, the results are comparable, objective, and reliable. The aim behind this is to enable evaluation using statistical techniques. In contrast to qualitative interviews, quantitative surveys provide less information about the causes of the phenomena under investigation (Döring & Bortz, 2016, pp. 181-184).

3.2. Research setting

The importance of online retail and thus of parcel delivery is continuously increasing (Kennedy & Coughlan, 2006, pp. 516-517). The aim of this study is to find out which characteristics of parcel delivery staff are important for customer satisfaction and loyalty. To this end, a survey is conducted to ask people living in Austria about their attitudes towards parcel delivery. The inquiry is conducted online using LimeSurvey. The LimeSurvey software ensures that the questions are displayed in the same order for each participant. A link to the questionnaire was sent to distribution lists to which the author has access. The people on these lists were asked to forward the link to the questionnaire to relatives, acquaintances, and friends. The aim was to motivate at least more than 100 people to complete the questionnaire (Saunders et al., 2019, pp. 188-189).

The questionnaire is divided into seven sections. At the beginning and end are questions on the demographic profile of the participants, followed by content-related questions on the topics of shopping behavior, satisfaction, loyalty, WOM, service quality (SERVQUAL), and shopper types. The SERVQUAL questions are divided into "expected" and "perceived" based on Parasuraman et.al. (1988). The empirical work only draws on the results of the questions concerning the „perceived service quality (Parasuraman et al., 1988, p. 19). The survey is developed in a way that allows respondents to complete it themselves. A seven-point Likert scale is used to answer the survey. All data will be analyzed using SmartPLS. The complete questionnaire ca be found in the appendix.

An online survey was chosen as the research instrument as it is an efficient and cost-effective method of measuring attitudes among a large number of persons. In addition, the survey process is standardized, which ensures consistent responses from a large group of people (Saunders et al., 2019, p. 504). To this end, Likert scales are used in the survey presented below, which simplifies the interpretation of the results. Moreover, online surveys are easy to administer, as participants can respond regardless of time and place and without the presence of the researcher (Saunders et al., 2019, p. 540). The absence of the research team can lead to difficulties in the interpretation of the questions by the participants.

In this Master's thesis, a research design was developed based on the literature analysis to determine customer perception using the SERVQUAL method. The questionnaire used, which was developed by Bearden & Netemeyer (1999), is divided into 22 items that were asked twice. The questions are asked both in relation to the expected and the perceived manifestation of the characteristics. The items were adapted to the research question of this Master's thesis. Respondents were able to indicate their expectations and perceptions on a seven-point scale from "strongly disagree = 1" to "strongly agree = 7" (Bearden & Netemeyer, 1999, pp. 402-405).

The descriptive evaluation, which only consists of the perceived values of all the SERVQUAL dimensions, is discussed below. This implies that the evaluation is similar to the approach of the SERVPERF framework (Cronin Jr. & Taylor, 1992, pp. 63-64).

In this empirical study, pretests were therefore carried out in which the survey was tested for comprehensibility by a small group of people (Saunders et al., 2019, p. 540). In addition, only previously tested questionnaires were used. A further challenge arises from the fact that it is difficult to reach some groups of people, which can lead to less representative results (Saunders et al., 2019, p. 314). Furthermore, participants must recall information from memory when answering the survey. However, people's recall is limited, especially if the events happened a long time ago. For this reason, this study also asks participants how often they use a delivery service. The non-response bias indicates that the respondents in the survey may differ from the average population in some characteristics, which can lead to distorted responses (Queiros et al., 2017, p. 381; Saunders et al., 2019, p. 236).

This survey consists of 95 questions (Q01 to Q95), most of which are composed of closed questions, matrix questions, and rating scales. An exception are two open questions on the average expenditure per purchase (Q08) and on working hours per week (Q95). These two questions had to be answered with a numerical value (Saunders et al., 2019, pp. 518-528). For question Q10, which involved the selection of product categories, several answers could be selected. Otherwise, only one answer option was allowed for each question. With one exception, there was never a "no answer" option. For the question on net monthly income (Q91), participants could choose whether they wanted to answer this question for reasons of ethics and data protection.

The following table contains the measurement scales that are part of the research model.

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Table 3: Measurement scales

3.3. Selecting the sample and data collection

Sampling is about collecting data from people who belong to certain subgroups of society. Approaches to sampling, such as quota or convenience sampling, are non-random procedures. They differ from random selection in that not every element has the same probability of being selected (Saunders et al., 2019, p. 292). In this study, a convenience sample was chosen as the technique. The aim of this methodology is to use a non-random procedure to achieve a similar distribution with regard to certain characteristics as in the population. The advantages of this method include its cost-effective implementation and rapid realization. The researchers can concentrate on groups of people that are easy to reach. A disadvantage often cited in the literature is that the results are difficult to generalize due to the over- or under-representation of certain subgroups (Saunders et al., 2019, p. 324).

For this study, the online survey was designed with LimeSurvey, which was hosted on JKU servers. Before sending access to the survey to the participants, a pilot test was carried out with five selected users. On the one hand, this served to check the technical functions and, on the other hand, to ensure that the content was comprehensible and complete (Saunders et al., 2019, pp. 540-541).

The following steps were taken to obtain participants for this study:

- Contact professors and teachers who can distribute the survey during their lectures.
- Distribute the survey on various social media platforms and groups.
- Share the survey with university student groups.
- Publish the survey to WhatsApp groups.
- Email the survey directly to friends and colleagues at work.

The groups mentioned are networking and exchange networks at the JKU on the one hand and groups at other Austrian universities on the other. In addition, the author sent invitations to participate directly to friends, acquaintances, and work colleagues and motivated them to complete the survey. This questionnaire aimed to survey 100 to 200 people. After about two weeks, the author realized that the resonance and response rates were low. For this reason, the author decided to send a personal reminder email, which increased the number of replies. As a result, 156 respondents were recruited and asked about their attitudes and satisfaction with parcel delivery persons. 112 participants completed the survey in full. People under the age of 16 were screened out of the survey. Another exclusion criterion was a lack of experience with delivery services. People who live outside Austria were also not permitted to take part. Due to the length of the questionnaire of 95 questions, it was necessary to carry out an attention test. People who answered this test question incorrectly were also eliminated from the study.

Concerning the representativeness of the survey, the aim was to achieve maximum gender balance. In terms of gender distribution, a result of 48 men, or 42.9 percent, and 64 women, or 57.1 percent, was achieved. Compared to an evaluation by Statistik Austria (2024, n.p.), in which the proportion of women in the Austrian population is 50.7 percent of woman and 49.3 percent of men, there is a slight difference. However, that does not affect the validity of this study.

3.4. Data analysis procedures

As the questionnaire consists exclusively of closed questions, SmartPLS and Excel were used to analyze the results.

The survey period lasted almost 7 weeks from November 22, 2023, to January 12, 2024. In order to ensure the comprehensibility of the questions, a pre-test was carried out with selected people from the circle of acquaintances before the implementation phase, which lasted about 1 week. After completion of the survey, the data sets were exported from LimeSurvey in CSV format to EXCEL. The recoding of the rating questions from the seven-part Likert scales also took place in EXCEL (Saunders et al., 2019, pp. 524-529).

The 30-day trial version of SmartPLS 4 was used for data analysis (Ringle et al., 2024, n.p.). The advantages of the software include its user-friendly operation, the ability to analyze surveys with small numbers of participants, and the opportunity to analyze interdependent constructs. This method is used to confirm theories. In this Master's thesis, the partial least square equation modeling algorithm (PLS-SEM), which is based on the analysis of variance, is used (Purwanto & Sudargini, 2021, p. 114).

Path models in SmartPLS each consist of a structural inner model and an outer measurement model (Hair et al., 2017, p. 32). In the inner model, the constructs are depicted as blue circles. It shows the paths between the constructs. These are also referred to as latent variables. The outer model, which consists of yellow rectangles, represents the relationship between the constructs and the indicators. While the inner model, also known as the measurement model, is used to test the reliability and validity of the results, the outer model can be used to estimate the strength of the effect (Hair et al., 2021, pp. 77-79). In the model, customer satisfaction represents the independent variable that is influenced by the dependent dimensions of the SERVQUAL method. The algorithm used aims to explain the greatest possible variation in the customer satisfaction variable. SmartPLS 4 uses bootstrapping to calculate the confidence intervals. The aim of this method is to test hypotheses. In this process, a new sample is repeatedly generated from the random sample by drawing it and putting it back. This is carried out several thousand times, resulting in a distribution of samples. The regression coefficients are estimated on the basis of these samples from the original data set. To test the correlation, the results are presented as a histogram and checked to determine whether the hypotheses can be confirmed (Hair et al., 2017, p. 103; Mendez-Suarez, 2021, p. 1832).

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Table 4: Methodological table

3.5. Ethical consideration

In research, ethics must always be considered as a behavioral norm, as it is important in all phases from the design of the study to the publication of the results (Saunders et al., 2019, p. 252).

An online questionnaire was chosen for participation in this study as part of this Master's thesis. The invitation to participate was sent by email and on social media channels. As part of the invite, respondents were informed that it was an anonymous survey to determine their satisfaction with parcel delivery companies (Saunders et al., 2019, pp. 248-249). The access link to the LimeSurvey questionnaire (https://survey.jku.at/214912?lang=de), which was hosted on the JKU servers, was identical for all participants. This implies that the identity of participants could not be tracked. Both in the invitation email and at the start of the survey the participants were informed about the background of the survey (Schroder & Brandenburg, 2019, pp. 127-128). Furthermore, the people taking part were informed that the data collected would be deleted again after the author's statistical analysis and that it would not be passed on to third parties (Schroder & Brandenburg, 2019, p. 130). In the LimeSurvey, no personal data such as e-mail address, IP address, etc. was collected or stored that would allow conclusions to be drawn about the identity of the respondent.

Participants had the option of cancelling the survey at any time. To ensure the anonymity of the people surveyed, no reward system was used (Saunders et al., 2019, pp. 257-258).

4. Results

This study aimed to investigate satisfaction with parcel delivery services. An online questionnaire was developed for this purpose, which can be found in the appendix. The results were calculated using SmartPLS 4 (v.4.1.0.7) and are the subject of the following chapter. A total of 156 people took part in this study. 112 respondents answered the questionnaire in full. 32 people only partially completed the survey or dropped out prematurely and were therefore excluded. The screen-out criteria applied to a further 12 respondents.

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Table 5: Statistics of survey participants

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Figure 6: Result LimeSurvey

4.1. Sample description

This chapter focuses on demographic aspects such as gender, age, place of residence, income, educational qualifications, occupation, and household size.

Q01: Gender

Out of the 112 people who took part in the survey, 57 percent were female and 43 percent male. The respondents also had the option of ticking diverse in the survey. However, none of the study participants identified with this category. Compared to the Austrian population, in which 50.7 percent are female, there is a slight difference (Statistik Austria, 2024, n.p.). However, this does not affect the validity of the survey.

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Table 6: Frequencies of gender (n=112)

Q02: Age

In terms of age, 25 to 34-year-olds make up the largest group with just under 30 percent. It is worth mentioning that people under the age of 16 were excluded from the survey. The group aged 16 to 24 was just as strongly represented in the study as the groups aged 35 to 44 and 45 to 54. As can be seen from the study, people older than 55 also enjoy shopping online. The exception is people over 65, who are underrepresented in this research. However, this cohort is also less likely to shop online, as can also be seen from a study by Statista (2023, n.p.).

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Q03: Main residence

In terms of place of residence, it should be noted that the ratio between urban and rural areas is almost balanced. Around 37 percent of respondents live in a larger city and around 39 percent in a rural community. Just under a quarter of respondents are based in a small town or suburb.

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Table 8: Frequencies of main residence (n=112)

Q91: Monthly income

When asked about their net salary, slightly less than half of respondents (45.5 percent) answered that they receive between 1200 and 2000 Euros per month. One in five (19.6 percent) surveyed have a monthly income of between 2000 and 2500 Euros. Eight percent of participants are paid between 2500 and 3000 Euros and only 3.5 percent of those generate over 3000 Euros each month. Six people surveyed decided not to give an answer, which was permitted for this question for data protection and ethical reasons.

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Table 9: Frequencies of monthly income (n=112)

4.2. Descriptive statistics

Descriptive data analysis is an important part of statistical methodology. The collected data is analyzed to identify patterns, trends and the statistical distribution of the data. Important key figures include e.g. valid, mean, standard deviation, median, minimum and maximum. Descriptive statistics include, for example, the graphical representation of data using boxplots and histograms (Fisher & Marshall, 2009, pp. 95-96; Saunders et al., 2019, pp. 597-602).

SERVQUAL

The following table provides an overview of the most important descriptive key figures of the five dimensions of the SERVQUAL model. The table also contains an overview of the individual questions and their description. The averages depicted below demonstrate that most consumers are quite satisfied with the quality of delivery services. However, minor differences between the dimensions can be noted.

The mean value for satisfaction with service quality is highest in the Reliability dimension at 5.27. The Assurance dimension follows directly behind with a mean value of 5.23. Tangibility has a mean value of 5.07 and is therefore in the mid-range. Responsiveness with 4.97 and Empathy with 4.83 are perceived less positively by the respondents.

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Table 10: Descriptive statistics of SERVQUAL (n=112)

Customer satisfaction, loyalty, word of mouth

In addition to the SERVQUAL dimensions, information on customer satisfaction, loyalty and word of mouth was also collected. The corresponding descriptive key figures are listed in the table below. However, the questions LOY_1 (Q26), LOY_2 (Q27) and LOY_3 (Q28) are phrased negatively and therefore have to be excluded from the analysis. Analyzing customer satisfaction gives a mean score of 5.10. Loyalty with a mean of 4.67 is ahead of word of mouth with a mean of 4.31.

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Table 11: Descriptive statistics of customer satisfaction word of mouth and loyalty (n=112)

4.3. Statistical testing

This section describes and evaluates the empirical model of this Master's thesis. The 112 valid results (n=112) on satisfaction with delivery services provide the foundation.

4.3.1. Reliability and validity of the model

The first step is to check the reliability and validity of the measurement model. This model was determined using the PLS algorithm. First, the outer loading is presented. Reliability and validity are then analyzed (Hair et al., 2017, p. 108).

The following figure shows the relationships between the constructs of the model.

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Figure 7: Construct of the edited research model

Outer loading

When examining the reliability of the measurement model, it is necessary to look at the correlations between the constructs. Correlations can assume values between 0 and +1.0. Higher values mean a stronger connection (Hair et al., 2017, p. 108). Hair et al. (2017) recommend a correlation of 0.7 and above. According to Hair et al. (2017), items below an outer loading of 0.7 should be removed from the model to enable further analysis. Values between 0.4 and 0.7 could remain in the model, but are usually deleted, as removing them leads to an improvement in the reliability and validity of the model (Hair et al., 2017, p. 99). Items above a threshold of 0.95 indicate redundancy which is also problematic and should be removed from the model (Sarstedt et al., 2021, p. 17). In this Master's thesis, the following questions were removed from the measurement model because the value of the outer loading was below 0.7: AS_4, RES_4, CS_1, WOM_1. Additionally, the questions LOY_1, LOY_2 and LOY_3 were removed from the model because of their negative wording.

The following table shows the external loadings. As can be seen in the table below, all indicators display a correlation of greater than or equal to 0.7.

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Table 12: Outer loadings (n=112)

Variance Inflation Factor (VIF)

The variance inflation factor measures the extent of multicollinearity between variables (Hair et al., 2017, p. 291). Multicollinearity means that the significance of the individual constructs is distorted. This also implies that it is not possible to say exactly which influence is caused by which variable. According to Hair et al. (2017), the value of the VIF should be below 5 (Hair et al., 2017, p. 179). The following table shows that all VIF values are below 5 and therefore below the threshold value.

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Table 13: Variance inflation factor VIF (n=112)

Reliability analysis

The reliability of this model is tested using Cronbach's alpha and composite reliability. The difference between the two measures is that composite reliability overestimates the reliability of the model. This problem does not occur with Cronbach's alpha (Hair et al., 2017, p. 97).

Composite reliability can take on values between 0 and 1, with higher values meaning better internal consistency of the model. Internal consistency refers to the correlation of the questions in a question matrix. Values between 0.6 and 0.7 are acceptable. Values between 0.7 and 0.9 can be classified as particularly good. Values above 0.95, on the other hand, are not desirable (Hair et al., 2017, p. 97).

Another indicator that measures the internal consistency of the model is Cronbach's alpha. According to Hair et al. (2017), this ratio should have a value of over 0.7 (Hair et al., 2017, p. 97). Like composite reliability, Cronbach's alpha provides an estimate of how accurate or inaccurate a group of questions (items) is. One condition for the validity of Cronbach's alpha is that all items in a question group measure the same latent variable. The stronger the correlation between the items on average, the greater the internal consistency of a model (Homburg & Giering, 1998, p. 8).

As can be seen in the following table, all quality criteria of Cronbach's alpha and composite reliability are fulfilled.

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Table 14: Cronbach's Alpha and Composite Reliability (n=112)

Convergent Validity

To determine the convergent validity, the average variance extracted (AVE) is calculated. Convergent validity is about how well the model measures what it is supposed to measure. To this end, the correlation between two constructs that should correlate strongly with each other is measured. The lower limit for acceptable convergent validity is 0.50, so it should not be less than this (Hair et al., 2017, p. 106).

As can be seen in the following table, all constructs exceed the suggested threshold.

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Table 15: Average Variance Extracted AVE (n=112)

Discriminant validity

In this chapter, the discriminant validity is determined using the Fornell - Larcker criterion, the cross-loadings, and the Heterotrait-Monotrait ratio. Discriminant validity is concerned with differences between the constructs. The aim is to prove that question groups that measure different constructs do not correlate with each other (Hair et al., 2017, p. 9).

Fornell - Larcker Criterion

The Fornell - Larker criterion is based on the concept of average variance extracted (AVE). This ratio is fulfilled if the common variance between the question groups and their indicators is greater than the correlation with another latent variable (=LVC) (Fornell & Larcker, 1981, p. 46). Discriminant validity is given when the square root of the average variance extracted (AVE) yields a higher value than a latent variable (Hair et al., 2017, p. 100).

As shown in the following table, discriminant validity is achieved.

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Table 16: Discriminant Validity - Fornell and Larcker Criterion (n=112)

Cross loadings

The objective of cross-loading is to demonstrate the correlation between the indicator in question and to evaluate the correlation with the other indicators. The correlation with the respective assigned indicator exhibits the highest degree of correlation, while the correlation with the other indicators is comparatively lower (Ab Hamid et al., 2017, pp. 2-3). The following table illustrates the cross-loadings of the constructs, thereby demonstrating the presence of discriminant validity.

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Table 17: Discriminant Validity - Cross loadings (n=112)

Heterotrait-Monotrait Ratio (HTMT)

The HTMT criterion calculates the ratio of the correlations between different characteristics (heterotrait) to the correlations within the same characteristic (monotrait). In this way, it reveals any problems with the multicollinearity of constructs. By comparing the HTMT ratios, it can be determined whether the latent variables are different and measure other constructs. The limit value should be below 0.9, ideally the result would be less than 0.85 (Hair et al., 2017, pp. 102-103). As can be seen from the following table, the values proposed by Hair et al. (2017) are not exceeded.

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Table 18: Discriminant Validity - HTMT (n=112)

The following figure shows the outer loadings, path coefficients (P) and Cronbach's alpha.

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Figure 8: Structural model with outer loadings, path coefficient and Cronbach’s Alpha (n=112)

4.3.2. Hypothesis test and predictive relevance of the model

To evaluate the measurement model, the p-value, also known as the path coefficient, the t-value, which is determined by bootstrapping, and the p-value, which expresses the statistical significance, are used (Farooq et al., 2018, pp. 175-176; Hu et al., 2019, p. 11).

The significance is tested using a 95 percent confidence interval, which corresponds to a significance level of 0.05. This means that the p-value should be less than 0.05 to verify the hypothesis (Andrade, 2019, p. 211; Hair et al., 2017, pp. 201-202).

Hypothesis H1: There is a positive relationship between the tangibility (TA) of the delivery person and customer satisfaction (CS).

According to the results below, TA has a significant influence on the CS scale. The p-value = 0.039 and thus < 0.05 (B=0.183, t=2.062, p = 0.039). Therefore, H1 is supported.

Hypothesis H2: There is a positive relationship between the reliability (REL) of the delivery person and customer satisfaction (CS).

As can be seen from the table below, REL has a considerable influence on the CS scale. The p- value = 0.031 and therefore < 0.05 (B=0.328, t=2.161, p = 0.031). Consequently, H2 is supported.

Hypothesis H3: There is a positive relationship between the assurance (AS) of the delivery person and customer satisfaction (CS).

The p-value of the AS scale is p=0.772 and is > 0.05 (B=0.039, t=0.290, p = 0.772). AS therefore has no noteworthy influence on the CS scale. Therefore, H3 is not supported.

Hypothesis H4: There is a positive relationship between the empathy (EM) of the delivery person and customer satisfaction (CS).

According to the result below, EM has no significant influence on the CS scale. The p-value = 0.590 and therefore > 0.05 (B=0.068, t=0.539, p = 0.590). Consequently, H4 is not supported.

Hypothesis H5: There is a positive relationship between the responsiveness (RES) of the delivery person and customer satisfaction (CS).

The p-value of the RES scale is p=0.569 and is > 0.05 (B=0.073, t=0.570, p = 0.569). RES therefore has no major influence on the CS scale. Consequently, H5 is not supported.

Hypothesis H6: There is a positive relationship between customer satisfaction (CS) and attitudinal outcomes (WOM).

According to the results from the table below, CS has a considerable influence on the WOM scale. The p-value = 0.000 and thus < 0.05 (B=0.576, t=10.882, p = 0.000). Therefore, H6 is supported.

Hypothesis H7: There is a positive relationship between customer satisfaction (CS) and intentional outcomes (LOY).

As can be seen from the results below, CS has a significant influence on the LOY scale. The p- value = 0.000 and therefore < 0.05 (B=0.437, t=5.629, p = 0.000). Consequently, H7 is supported.

The following table contains a summary of the hypothesis tests and shows a graphical representation of the model with the B-values and t-values.

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Table 19: Result Hypothesis - Test (n=112)

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Figure 9: Structural model with B-values ant t-values (n=112)

Prediction power (R[2])

The R[2] measure is calculated using regression analysis and can assume values between 0 and 1. It indicates the proportion of the variance of the independent variables that can be explained by the dependent variables. Higher values show a greater magnitude of effect. The predictive power of the model is evaluated through the examination of the R2 value. This illustrates the impact of the exogenous latent variable CS on the two endogenous latent variables, WOM and LOY. According to Hair et al. (2017), values between 0.1 and 0.25 correspond to weak effects, up to 0.74 for moderate effects, and from 0.75 onwards to strong effects (Hair et al., 2017, p. 171).

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Table 20: R2 values of the endogenous latent variables (n=112)

The effect size f2

Cohen's effect size f2 is a key figure that is used for multiple linear regressions. Its purpose is to determine whether an independent variable influences the dependent variable (Ringle & Spreen, 2007, p. 215). For the evaluation of the results, values of 0.02, 0.15, and 0.35 are cited in the literature, which means small, medium, and large effects (Hair et al., 2017, p. 172).

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Table 21: Effect Size, F-Square (n=112)

Stone-Geisser Q[2] value

The Stone-Geisser Q[2] value indicates the predictive relevance of the model. According to Hair et al. (2017), the threshold value that shows predictive relevance is greater than 0. SmartPLS determines this value with the help of blindfolding using the PLSpredict command (Hair et al., 2017, p. 174). The actual data is compared with the prediction errors (Gotz & Liehr-Gobbers, 2004, p. 731).

As can be seen in the table below, all values are above 0.0, which shows that the model is effective for prognosis and can describe the underlying structure of the data well.

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Table 22: Stone-Geisser Q[2] Values (n=112)

4.3.3. Model test with control variables

The next step was to add the four control variables Q01 (Gender), Q02 (Age), Q03 (Residence) and Q91 (Monthly income) to the model.

The aim is to test whether these four variables affect customer satisfaction (CS).

Gender (Q01) as a control variable proves to be not significant and does not affect the dependent variable CS. The bootstrapping results (5000 repetitions) in the following table show that gender has no influence on customer satisfaction (CS) (B=0.105, t=0.528, p=0.597).

Age (Q02) as a control variable is not significant and has no effect on the dependent variable CS. The bootstrapping results (5000 repetitions) in the following table show that age does not influence customer satisfaction (CS) (B=-0.093, t=1.119, p=0.263).

The place of residence (Q03) as a control variable proves to be not significant and does not affect the dependent variable CS. The bootstrapping results (5000 repetitions) in the following table show that place of residence has no influence on customer satisfaction (CS) (B=-0.008, t=0.122, p=0.903).

Net monthly income (Q91) as a control variable is not significant and has no effect on the dependent variable CS. The bootstrapping results (5000 repetitions) in the following table show that monthly net income does not influence customer satisfaction (CS) (B=0.029, t=0.357, p=0.721).

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Table 23: Test Control Variables to CS (n=112)

The following figure illustrates the relationships between the latent variables CS and the control variables Q01, Q02, Q03, Q91.

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Figure 10: Control Variables to CS with the B-values and t-values (n=112)

Subsequently, the effects of the four control variables on the latent variable WOM are examined. Gender (Q01) as a control variable proves to be not significant and has no effect on the dependent variable WOM. The bootstrapping results (5000 repetitions) in the following table show that gender has no influence on attitudinal outcomes (WOM) (B=0.095, t=0.634, p=0.526).

Age (Q02) as a control variable is not significant and has no effect on the dependent variable WOM. The bootstrapping results (5000 repetitions) in the following table show that age has no influence on attitudinal outcomes (WOM) (B=0.071, t=1.002, p=0.316).

The place of residence (Q03) as a control variable proves to be not significant and has no effect on the dependent variable WOM. The bootstrapping results (5000 repetitions) in the following table show that place of residence has no influence on attitudinal outcomes (WOM) (B=-0.141, t=1.935, p=0.053).

Net monthly income (Q91) as a control variable is not significant and has no effect on the dependent variable WOM. The bootstrapping results (5000 repetitions) in the following table show that monthly net income has no influence on attitudinal outcomes (WOM) (B=0.145, t=1.877, p=0.061).

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Table 24: Test Control Variables to WOM (n=112)

In the last step, the effects of the four control variables on the latent variable LOY are examined. Gender (Q01) as a control variable proves to be not significant and has no effect on the dependent variable LOY. The bootstrapping results (5000 repetitions) in the following table show that gender has no influence on intentional outcomes (LOY) (B=0.176, t=0.939, p=0.348).

Age (Q02) as a control variable is not significant and has no effect on the dependent variable LOY. The bootstrapping results (5000 repetitions) in the following table show that age has no influence on intentional outcomes (LOY) (B=0.142, t=1.647, p=0.100).

The place of residence (Q03) as a control variable proves to be not significant and has no effect on the dependent variable LOY. The bootstrapping results (5000 repetitions) in the following table show that place of residence has no influence on intentional outcomes (LOY) (B=0.027, t=0.336, p=0.737).

Net monthly income (Q91) as a control variable is not significant and has no effect on the dependent variable LOY. The bootstrapping results (5000 repetitions) in the following table show that monthly net income has no influence on intentional outcomes (LOY) (B=0.105, t=1.004, p=0.315).

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Table 25: Test Control Variables to LOY (n=112)

5. Conclusion

At the beginning of this chapter, the empirical results on satisfaction with parcel services are compared with insights from theory. The contributions of this research to theory and management practice are outlined next. Finally, the limitations of the research design are discussed and recommendations for further research are derived.

5.1. Theoretical and practical implications

5.1.1. Theoretical implications and discussion

The aim of this Master's thesis was to measure the relationship between the service quality of parcel deliverers and the satisfaction, loyalty and recommendation of delivery services. Responding to the research question “What characteristics of delivery persons influence customer satisfaction?”, an online questionnaire with predominantly closed questions was designed. The evaluation of the survey focused on the perceived service quality of customers, which was measured using the five dimensions of the SERVQUAL model “reliability”, “responsiveness”, “assurance”, “empathy” and “tangibility” (Parasuraman et al., 1988, pp. 23-24). In terms of satisfaction with delivery service employees, this empirical study shows that the dimensions “reliability” and “tangibility” have a significant influence on customer satisfaction. The dimension “tangibility” has to do with the recognizability of the employees of delivery services through tangible aspects such as the wearing of work clothes. In addition, this dimension is about the equipment and cleanliness of the vehicle and the use of modern equipment. In terms of reliability, adherence to the delivery date and error-free documentation are of importance to customers. Customers expect this reliability from the delivery company for every delivery, regardless of the delivery person.

The study conducted as part of this Master's thesis thus confirms the findings of Yee & Daud (2011), according to which the dimensions of “tangibility”, “assurance” and “reliability” have a positive influence on customer satisfaction (Yee & Daud, 2011, pp. 7-8). With regard to satisfaction with delivery services, Gulc (2020) found that different customer groups have different needs and therefore there are no universally valid factors that positively influence satisfaction (Gulc, 2020, pp. 140-142). In general, however, the literature research reveals that all five factors of the service quality model are suitable for explaining satisfaction with delivery services (Siali et al., 2018, p. 430). In addition, employee satisfaction and the perceived value of the service influence customer satisfaction with parcel services (Akram et al., 2022, pp. 134-135; Uzir et al., 2021, pp. 7-10).

Customers compare the service they receive with their expectations. According to the “expectation disconfirmation theory”, in order to satisfy customers, you have to positively surprise them and trigger a wow effect. If the customer's expectations are met, this leads to neutral feelings towards the service provider (Almsalam, 2014, pp. 79-80; Rao & Sahu, 2013, p. 40). If the delivery company succeeds in positively surprising the customer with its service, this increases trust in the company, which in turn leads to loyalty. The image of the delivery company also influences customer loyalty (Akram et al., 2022, pp. 134-135).

Trustworthy partners can have a positive impact on the creativity and the core competencies of the company. Trust even helps reduce costs and increase the effectiveness and efficiency of business collaborations. The presence of trust reduces the need for control, which is one reason for lower costs. Furthermore, trust between partners influences customer satisfaction. The outcomes of trust are higher cooperation, higher customer satisfaction and higher loyalty (Mazzola & Perrone, 2013, pp. 258-259; Palvia et al., 2010, p. 238; Sambasivan et al., 2011, pp. 550-555).

As far as trust and loyalty are concerned, the study conducted in this Master's thesis found that service quality and customer satisfaction have a positive influence on loyalty. The studies by Hamidin & Hendrayati (2022) and Correa et al. (2021) also show that service quality exhibits a positive impact on trust in delivery services and thus has a positive influencing effect on loyalty (Akram et al., 2022, pp. 134-135; Correa et al., 2021, pp. 13-14; Hamidin & Hendrayati, 2022, p. 287).

Concerning the effect of word of mouth, this study shows that service quality also increases the probability that users of delivery services will tell their friends, co-workers and acquaintances positively about their experiences. In general, it can be concluded that there are hardly any scientific studies on word of mouth in the parcel delivery industry. However, the positive effect of service quality on word of mouth in the service sector has been proven by numerous other studies (Susilowati & Yasri, 2019, p. 678; Wang, 2011, p. 256; Yasri & Engriani, 2018, pp. 354-355).

The quality of parcel delivery services influences customer satisfaction as well as loyalty and word of mouth. The research conducted in this Master's thesis shows that by prioritizing high-quality service and sustainable relationships, parcel delivery companies can positively influence customer satisfaction, loyalty, and word of mouth.

5.1.2. Practical implications

Organizational framework

As can be seen from the previous chapters, service quality plays a decisive role in ensuring customer satisfaction with parcel services. The empirical study shows that the dimensions of “tangibility” and “reliability” in particular have an influence on customer satisfaction. Furthermore, the study shows that a neat appearance of the delivery person is essential to customers. This means that companies must pay attention to offering the desired service. Parcel carriers are not always able to meet their customers' demands for punctual and reliable service due to organizational and technological constraints. One potential reason for this is that parcel delivery staff are employed by subcontractors and not by the headquarters. In this context, the efficient design of organizational frameworks and processes that are geared towards the fast and error- free delivery of parcels is essential. These are processes that are of immense value in the eyes of customers. Modern infrastructure plays a significant role here. A modern network of pick-up and drop-off stations, modern information systems that enable parcel tracking, and sorting facilities are crucial.

Feedback and reward system

Yang (2024) emphasizes that employees are often insufficiently trained in customer-friendly behavior. In addition, a reward system should be implemented to motivate delivery service employees to meet deadlines and demonstrate customer-friendly behavior. In addition, rewards should be directly linked to customer feedback to motivate employees to continuously improve their interaction with customers. It helps them predict and respond to changing customer needs and market conditions.

Customer friendly behavior

Employees who recognize and respond to individual customer needs strengthen trust in the delivery service. It is also necessary to make the complaints process as simple as possible. In addition, parcel service employees should be able to pass on information such as delays in parcel delivery directly to the customer. Direct contact is particularly important if there are delays in parcel delivery. Li et al. (2004) emphasize that direct parcel delivery is particularly important to the customer. In the event of a failed delivery attempt, collection from a parcel station is very time­consuming and should therefore be avoided. Parcel service managers should also ensure that the process for changing the delivery address is standardized and as simple as possible.

5.2. Limitations and future research

Despite the valuable knowledge of the research for use in practice and the significance for the marketing of services, this research is also subject to certain limitations. The survey, which forms the basis for the empirical findings, was completed by a limited number of people from Austria. Future research in this area should include a larger sample and pay attention to diversity in terms of customer groups and origin among the survey participants. In this context, an adaptation of the SERVQUAL- model might also be necessary. This would also improve the validity of the results.

Also, this study focuses on the perception, but not the expectation, of service quality by customers. It is therefore advisable to include the perspectives of other stakeholders in the delivery process in order to ensure a comprehensive understanding of service quality. Of particular interest in this regard is the perspective of online stores that contract delivery services, but that are nevertheless responsible for the delivery to the customer. In that context it could also be interesting to do research on how the online store influences customer satisfaction.

The role of technology in courier services is of immense importance due to the rapid pace of technological progress. In the future, drones and parcel stations will play a greater role in parcel delivery. It is therefore worth investigating their influence on service quality and customer satisfaction. To increase customer satisfaction, delivery companies must offer different delivery options. The influence of different product groups on service quality and customer satisfaction is furthermore of great interest. In this regard, customer needs and their influence on parcel delivery are important.

List of references

Ab Hamid, M. R., Sami, W., & Mohmad Sidek, M. H. (2017). Discriminant Validity Assessment: Use of Fornell & Larcker criterion versus HTMT Criterion. Journal of Physics: Conference Series, 890, 012163. https://doi.org/10.1088/1742-6596/890/1/012163

Abedin, M., Rahman, N., & Mohiuddin, M. (2016). Service Quality Level and the Perception of Customers: A study on Nijhoom Tours-5* Rated Travel and Tourism Company in Bangladesh. International Journal of Management Sciences and Business Research, 5 (11), 109-118. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2880403

Adat, N., Noel, D. T., & Penceliah, S. (2014). Customers’ expectations and perceptions of service quality: The case of a retail pharmacy chain in South Africa. Mediterranean Journal of Social Sciences, 2648-2655. https://hdl.handle.net/10321/1473

Ahn, C. K., Kang, C. W., Park, J. H., & Rim, S. T. (2009). An application of human performance technology: A case study at LG CNS. Journal of Service Science, 1, 211-225. https://doi.org/10.1007/s12927-009-0010-2

Akram, M. W., Abbas, A., & Khan, I. A. (2022). Effects of Perceived Value, Service Quality and Customer Trust in Home Delivery Service Staff on Customer Satisfaction: Evidence from Pakistan. International Journal of Management Research and Emerging Sciences, 12 (4). https://doi.org/10.56536/ijmres.v12i4.351

Alexander, A., Teller, C., & Roggeveen, A. L. (2016). The boundary spanning of managers within service networks. Journal of Business Research, 69 (12), 6031- 6039. https://doi.org/10.1016/j.jbusres.2016.05.018

Alhelalat, J. A., Habiballah, M. A., & Twaissi, N. M. (2017). The impact of personal and functional aspects of restaurant employee service behaviour on customer satisfaction. I nternational Journal of Hospitality Management, 66, 46-53. https://doi.org/10.1016/j.ijhm.2017.07.001

Ali, B. J., Saleh, P. F., Akoi, S., Abdulrahman, A. A., Muhamed, A. S., Noori, H. N., & Anwar, G. (2021). Impact of Service Quality on the Customer Satisfaction: Case study at Online Meeting Platforms. International Journal of Engineering, Business and Management. https://doi.org/10.22161/ijebm.5.2

Al-Ja’afreh, A. L. I., & Al-Adaileh, R. A. I. D. (2020). The impact of electronic word of mouth on consumers purchasing intention. Journal of Theoretical and Applied Information Technology, 98 (02), 183-193. http://www.jatit.org/volumes/Vol98No2/2Vol98No2.pdf

Al-Jazzazi, A., & Sultan, P. (2017). Demographic differences in Jordanian bank service quality perceptions. Int. J. Bank Mark, 35, 275-297. https://doi.org/10.1108/IJBM-07-2016-0091

Almsalam, S. (2014). The effects of customer expectation and perceived service quality on customer satisfaction. International journal of business and management invention, 3(8), 79-84

Amyx, D., Bhuian, S. N., & Shows, G. D. (2016). Customer-salespeople relationship: Influence of salespeople entrepreneurial behaviours. Marketing Intelligence & Planning, 34 (5), 586-604. https://doi.org/10.1108/MIP-09-2015-0170

Anderson, S. W., Chang, H. F., Cheng, M. M., & Phua, Y. S. (2017). Getting to know you: Trust formation in new interfirm relationships and the consequences for investments in management control and the collaboration. Contemporary Accounting Research, 34 (2), 940-965. https://doi.org/10.1111/1911-3846.12282

Andrade, C. (2019). The P Value and Statistical Significance: Misunderstandings, Explanations, Challenges, and Alternatives. Indian Journal of Psychological Medicine, 41 (3), 210-215.

https://doi.org/10.4103/IJPSYM.IJPSYM_193_19

Bansal, H. S., Irving, P. G., & Taylor, S. F. (2004). A three-component model of customer to service providers. Journal of the Academy of marketing Science, 32 (3), 234-250. https://doi.org/10.1177/0092070304263332

Barrett, M., Davidson, E., Prabhu, J., & Vargo, S. L. (2015). Service innovation in the digital age. MIS quarterly, 39 (1), 135-154. https://www.jstor.org/stable/26628344

Basir, M. S., Ahmad, S. Z., & Kitchen, P. J. (2010). The Relationship between sales skills and salesperson performance: an empirical study in the Malaysia Telecommunications Company. International Journal of Management and Marketing Research, 3 (1), 51-73. https://ssrn.com/abstract=1668838

Bearden, W. O., & Netemeyer, R. G. (1999). Handbook of marketing scales: multi-item measures for marketing and consumer behavior research. Association for Consumer Research series Show all parts in this series. 404-405

Benuisiene, I., & Petukiene, E. (2012). The indicators of service quality measurement of logistics services. The journal of Social research, 2(27), 62-70.

https://epublications.vu.lt/object/elaba:6100393

Bettencourt, L. A., & Brown, S. W. (1997). Contact employees: Relationships among workplace fairness, job satisfaction and prosocial service behaviors. Journal of Retailing, 73(1), 39-61. https://doi.org/10.1016/S0022-4359(97)90014-2

Bitner, M. J. (1990). Evaluating service encounters: the effects of physical surroundings and employee responses. Journal of marketing, 54 (2), 69-82.

https://www.ida.liu.se/~steho87/und/htdd01/9602205187.pdf

Bitner, M. J., Brown, S. W., & Meuter, M. L. (2000). Technology infusion in service encounters. Journal of the Academy of marketing Science, 28 (1), 138-149.

https://doi.org/10.1177/0092070300281013

Blery, E., Batistatos, N., Papastratou, E., Perifanos, I., Remoundaki, G., & Retsina, M. (2009). Service quality and customer retention in mobile telephony. Journal of Targeting, Measurement and Analysis for Marketing, 17, 27-37. https://doi.org/10.1057/jt.2008.26

Bowen, J. T., & Chen, S. L. (2001). The relationship between customer loyalty and customer satisfaction. International journal of contemporary hospitality management, 13(5), 213-217. https://doi.org/10.1108/09596110110395893

Bowersox, D. J., Closs, D. J., & Stank, T. P. (2000). Ten mega-trends that will revolutionize supply chain logistics. Journal of business logistics, 21 (2), 1.

https://www.researchgate.net/publication/248649433_Ten_Mega-Trends_That_Will_Revolutionize_Supply_Chain_Logistics

Brown, T. J., Churchill Jr., G. A., & Peter, J. P. (1993). Research note: improving the measurement of service quality. Journal of retailing, 69 (1), 127. https://doi.org/10.1016/S0022- 4359(05)80006-5

Caruana, A. (2002). Service loyalty: The effects of service quality and the mediating role of customer satisfaction. European Journal of Marketing, 36(7/8), 811-828. https://doi.org/10.1108/03090560210430818

Castaldo, S., Premazzi, K., & Zerbini, F. (2010). The meaning (s) of trust. A content analysis on the diverse conceptualizations of trust in scholarly research on business relationships. Journal of business ethics, 96, 657-668. https://doi.org/10.1007/s10551-010-0491-4

Castleberry, S. B., & Shepherd, C. D. (1993). Effective interpersonal listening and personal selling. Journal of Personal Selling & Sales Management, 13 (1), 35-49. http://dx.doi.org/10.1080/08853134.1993.10753935

Cater, T., & Cater, B. (2010). Product and relationship quality influence on customer commitment and loyalty in B2B manufacturing relationships. Industrial Marketing Management, 39 (8), 1321-1333. https://doi.org/10.1016/j.indmarman.2010.02.006

Chen, Y., Chen, C. Y., & Hsieh, T. (2007). Correlation of service quality, customer satisfaction, customer loyalty and life style at hot springs hotels. Journal of International Management Studies, 2 (2), 51-59

Chen, Z., Huang, Y., & Sternquist, B. (2011). Guanxi practice and Chinese buyer-supplier relationships: The buyer’s perspective. Industrial Marketing Management, 40(4), 569-580. https://doi.org/10. 1016/j.indmarman.2010.12.013

Choi, K. S., Cho, W. H., Lee, S., Lee, H., & Kim, C. (2004). The relationships among quality, value, satisfaction and behavioral intention in health care provider choice: A South Korean study. Journal of business research, 57 (8), 913-921. https://doi.org/10.1016/S0148-2963(02)00293-X

Chou, S. W., Techatassanasoontorn, A. A., & Hung, I. H. (2015). Understanding commitment in business process outsourcing relationships. Information & Management, 52 (1), 30-43. https://doi.org/10.1016/j.im.2014.10.003

Churchill Jr., G. A., Ford, N. M., Hartley, S. W., & Walker Jr, O. C. (1985). The determinants of salesperson performance: A meta-analysis. Journal of marketing research, 22 (2), 103-118. https://doi.org/10.1177/002224378502200201

Correa, C., Alarcon, D., & Cepeda, I. (2021). “I am Delighted!”: The Effect of Perceived Customer Value on Repurchase and Advocacy Intention in B2B Express Delivery Services. Sustainability, 13 (11), 1-19. https://doi.org/10.3390/su13116013

Cronin Jr., J. J., & Taylor, S. A. (1992). Measuring service quality: a reexamination and extension. Journal of marketing, 56 (3), 55-68. https://doi.org/10.1177/002224299205600304

Cropanzano, R., & Mitchell, M. S. (2005). Social exchange theory: An interdisciplinary review. Journal of management, 31 (6), 874-900. https://doi.org/10.1177/0149206305279602

Dabholkar, P. A., Shepherd, C. D., & Thorpe, D. I. (2000). A comprehensive framework for service quality: an investigation of critical conceptual and measurement issues through a longitudinal study. Journal of retailing, 76 (2), 139-173. https://doi.org/10.1016/S0022- 4359(00)00029-4

Darmon, R. Y. (2008). The concept of salesperson replacement value: A sales force turnover management tool. Journal of Personal Selling & Sales Management, 28 (3), 211-232. https://doi.org/10.2753/PSS0885-3134280301

Dehghan, A., Shahin, A., & Zenouzi, B. (2011). Service quality gaps & six sigma. Management Research, 4 (1). http://dx.doi.org/10.5296/jmr.v4i1.887

DiGaetani, J. L. (1980). The business of listening. Business Horizons, 23 (5), 40-46. https://doi.org/10.1016/0007-6813(80)90070-1

Döring, N., & Bortz, J. (2016). Forschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften (5th ed.). Springer-Verlag. https://doi.org/10.1007/978-3-642-41089-5

Dyer, J., & Chu, W. (2000). The Determinants of Trust in Supplier-Automaker Relationships in the U.S., Japan and Korea. J Int Bus Stud 31,259-285. https://doi.org/10.1057/palgrave.jibs.8490905

Eger, L., Komàrkovà, L., Egerovà, D., & Micik, M. (2021). The effect of COVID-19 on consumer shopping behaviour: Generational cohort perspective. Journal of Retailing and Consumer Services, 61, 102542. https://doi.org/10.1016/j.jretconser.2021.102542

Endeshaw, B. (2019). A review on the existing service quality measurement models. Science Journal of Business and Management,7(4), 87-201. https://doi.org/10.11648/j.sjbm.20190704.13

Esteban-Bravo, M., & Vidal-Sanz, J. M. (2021). Marketing research methods: Quantitative and qualitative approaches. Cambridge University Press

Fairlie, R., & Fossen, F.M. (2022). The early impacts of the COVID-19 pandemic on business sales. Small Bus Econ 58, 1853-1864. https://doi.org/10.1007/s11187-021-00479-4

Farooq, M. S., Salam, M., Fayolle, A., Jaafar, N., & Ayupp, K. (2018). Impact of service quality on customer satisfaction in Malaysia airlines: A PLS-SEM approach. Journal of Air Transport Management, 67, 169-180. https://doi.org/10.1016/j.jairtraman.2017.12.008

Fida, B. A., Ahmed, U., Al-Balushi, Y., & Singh, D. (2020). Impact of service quality on customer loyalty and customer satisfaction in Islamic banks in the Sultanate of Oman. Sage Open, 10 (2). https://doi.org/10.1177/2158244020919517

Fisher, M. J., & Marshall, A. P. (2009). Understanding descriptive statistics. Australian Critical Care, 22 (2), 93-97. https://doi.Org/10.1016/j.aucc.2008.11.003

Flint, D. J., & Mentzer, J. T. (2000). Logiticians as marketers: Their role when customers' desired value changes. Journal of Business Logistics, 21 (2), 19

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18 (1), 39-50. https://doi.org/10.1177/002224378101800104

Franceschini, F., & Mastrogiacomo, L. (2018). Service gap deployment: a framework to link quality gaps to service activities. International Journal of Services and Operations Management, 29 (1), 85-99. https://doi.org/10.1504/IJSOM.2018.088474

Franke, G. R., & Park, J. E. (2006). Salesperson adaptive selling behavior and customer orientation: A meta-analysis. Journal of marketing Research, 43 (4), 693-702. https://doi.org/10.1509/jmkr.43.4.693

Fullerton, G. (2014). The moderating effect of normative commitment on the service quality­customer retention relationship. European journal of marketing, 48 (3/4), 657-673. https://doi.org/10.1108/EJM-06-2011-0333

Fullerton, G., & Taylor, S. (2002). Mediating, interactive, and non-linear effects in service quality and satisfaction with services research. Canadian journal of administrative sciences/revue canadienne des sciences de l'administration, 19 (2), 124-136. https://doi.org/10.1111/j.1936- 4490.2002.tb00675.x

Geyskens, I., Steenkamp, J.b., & Kumar, N. (1999). A Meta-Analysis of Satisfaction in Marketing Channel Relationships. Journal of Econometrics -J ECONOMETRICS. 36(2). 223-238. https://doi.org/10.2307/3152095

Ghoumrassi, A., & Tigu, G. (2017). The Impact of the Logistics Management in Customer Satisfaction. Proceedings of the International Conference on Business Excellence, 11(1), 292­301. https://doi.org/10.1515/picbe-2017-0031

Giese, J. L., & Cote, J. A. (2000). Defining consumer satisfaction. Academy of marketing science review, 1 (1), 1-22. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=bd40d475aab7b417fd282631 3b98be1f214ba45a

Gilmore, A., & Carson, D. (1996). Management competences for services marketing. Journal of Services Marketing, 10 (3), 39-57. https://doi.org/10.1108/08876049610119785

Götz, O., & Liehr-Gobbers, K. (2004). Analyse von strukturgleichungsmodellen mit hilfe der partial-least-squares (PLS)-methode. Die Betriebswirtschaft, 64 (6), 714-738

Grönroos, C. (1984). A Service Quality Model and its Marketing Implications. European Journal of Marketing, 18(4), 36-44. https://doi.org/10.1108/EUM0000000004784

Grönroos, C. (2001). The perceived service quality concept-a mistake?. Managing Service Quality: An International Journal, 11 (3), 150-152. https://doi.org/10.1108/09604520110393386

Grönroos, C. (2007). “Service Management and Marketing: Customer Management in service Competition”, 3rd Edition, John Wiley & Sons Ltd., England, P. 483

Gulc, A. (2020). Determinants of Courier Service Quality in e-Commerce from Customers’ Perspective. Quality Innovation Prosperity, 24 (2), 137-152. https://doi.org/10.12776/qip.v24i2.1438

Gulc, A. (2021). Multi-stakeholder perspective of courier service quality in B2C e-commerce. PLoS One, 16(5), 1-18. https://doi.org/10.1371/journal.pone.0251728

Guthrie, C., Fosso-Wamba, S., & Arnaud, J. B. (2021). Online consumer resilience during a pandemic: An exploratory study of e-commerce behavior before, during and after a COVID-19 lockdown. Journal of Retailing and Consumer Services, 61, 102570. https://doi.org/10.1016/j.jretconser.2021.102570

Hair, J. F., Hauff, S., Hult, G. T. M., Richter, N. F., Ringle, C. M., & Sarstedt, M. (2017). Partial Least Squares Strukturgleichungsmodellierung. Verlag Franz Vahlen GmbH. https://doi.org/10.15358/9783800653614

Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook. Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7

Hamidin, D., & Hendrayati, H. (2022). Courier Service Application: Courier Service Quality and Customer Loyalty Mediated by Customer Experience and Customer Satisfaction. 6th Global Conference on Business, Management, and Entrepreneurship (GCBME 2021), 286-292. https://www.atlantis-press.com/proceedings/gcbme-21/125976000

Han, S. L., Sung, H. S., & Shim, H. S. (2014). Antecedents and performance outcomes of flexibility in industrial customer-supplier relationships. Journal of Business Research, 67 (10), 2115-2122. https://doi.org/10.1016/j.jbusres.2014.04.020

Hansen, H., Sandvik, K., & Selnes, F. (2003). Direct and indirect effects of commitment to a service employee on the intention to stay. Journal of Service Research, 5 (4), 356-368. https://doi.org/10.1177/1094670503005004007

Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of- mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing, 18 (1), 38-52. https://doi.org/10.1002/dir.10073

Ho, J. S. Y., Teik, D. O. L., Tiffany, F., Kok, L. F. & Teh, T. Y., (2012). Logistic Service Quality among Courier Services in Malaysia. International Proceedings of Economics Development & Research, 38(2012), 113-117

Ho, V. T., Ang, S., & Straub, D. (2003). When subordinates become IT contractors: Persistent managerial expectations in IT outsourcing. Information systems research, 14 (1), 66-86. https://doi.org/10.1287/isre.14.1.66.14764

Hocutt, M. A., & Stone, T. H. (1998). The impact of employee empowerment on the quality of a service recovery effort. Journal of quality management, 3 (1), 117-132. https://doi.org/10.1016/S1084-8568(99)80107-2

Homburg, C., & Giering, A. (1998). Konzeptualisierung und Operationalisierung komplexer Konstrukte: ein Leitfaden für die Marketingforschung

Hu, Z., Ding, S., Li, S., Chen, L., & Yang, S. (2019). Adoption Intention of Fintech Services for Bank Users: An Empirical Examination with an Extended Technology Acceptance Model. Symmetry, 11 (3), 340. https://doi.org/10.3390/sym11030340

Hussain, F., Rahman, M. B. B. S., & Hassan, M. G. B. (2012, July). Customer relationship management and information technology as determinants of Malaysian hospitality industry: A qualitative approach. international conference on green and ubiquitous technology 37-40. https://doi.org/10.1109/GUT.2012.6344183

Jain, S. K., & Gupta, G. (2004). Measuring service quality: SERVQUAL vs. SERVPERF scales. Vikalpa, 29 (2), 25-38. https://doi.org/10.1177/0256090920040203

Jamaluddin, J., & Ruswanti, E. (2017). Impact of service quality and customer satisfaction on customer loyalty: a case study in a private hospital in Indonesia. IOSR Journal of Business and Management,19(5), 23-33. https://www.iosrjournals.org/iosr-jbm/papers/Vol19-issue5/Version- 1/D1905012333.pdf

Jones, J. L., & Shandiz, M. (2015). Service quality expectations: Exploring the importance of SERVQUAL dimensions from different nonprofit constituent groups. Journal of Nonprofit & Public Sector Marketing, 27 (1), 48-69. https://doi.org/10.1080/10495142.2014.925762F

Joyner Armstrong, C. M., Kang, J., & Lang, C. (2018). Clothing style confidence: The development and validation of a multidimensional scale to explore product longevity. Journal of Consumer Behaviour, 17 (6), 553-568. https://doi.org/10.1002/cb.1739

Jun, M., Yang, Z., & Kim, D. (2004). Customers' perceptions of online retailing service quality and their satisfaction. International Journal of Quality & Reliability Management, 21(8), 817-840. https://doi.org/10.1108/02656710410551728

Kant, R., & Jaiswal, D. (2017). The impact of perceived service quality dimensions on customer satisfaction: An empirical study on public sector banks in India. Int. J. Bank Mark, 35, 411-430. https://doi.org/10.1108/IJBM-04-2016-0051

Karatepe, O. M. (2011). Service quality, customer satisfaction and loyalty: the moderating role of gender. Journal of Business Economics and Management, 12 (2), 278-300. https://doi.org/10.3846/16111699.2011.573308

Kashif, M., Altaf, U., Ayub, H. M., Asif, U., & Walsh, J. C. (2014). Customer satisfaction at public hospitals in Pakistan: PAKSERV application. Global Business Review, 15(4), 677-693. https://doi.org/10.1177/0972150914543556

Keillor, B. D., Parker, R. S., & Pettijohn, C. E. (1999). Sales force performance satisfaction and aspects of relational selling: Implications for sales managers. Journal of Marketing Theory and Practice, 7 (1), 101-115. https://doi.org/10.1080/10696679.1999.11501824

Keller, S. B. (2002). Driver Relationships With Customers and Driver Turnover: Key Mediating Variables Affecting Driver Performance in the Field. Journal of Business Logistics, 23(1), 39-64. https://doi.org/10.1002/j.2158-1592.2002.tb00015.xeller

Kennedy, A., & Coughlan, J. (2006). Online shopping portals: an option for traditional retailers?. International Journal of Retail & Distribution Management, 34 (7), 516-528. https://doi.org/10.1108/09590550610673590

Khan, I., Garg, R. J., & Rahman, Z. (2015). Customer service experience in hotel operations: An empirical analysis. Procedia-Social and Behavioral Sciences, 189, 266-274. https://doi.org/10.1016/j.sbspro.2015.03.222

Khan, M. M., & Fasih, M. (2014). Impact of service quality on customer satisfaction and customer loyalty: Evidence from banking sector. Pakistan Journal of Commerce and Social Sciences (PJCSS), 8 (2), 331-354

Khorshidi, H. A., Nikfalazar, S., & Gunawan, I. (2016). Statistical process control application on service quality using SERVQUAL and QFD with a case study in trains’ services. The TQM

Journal, 28 (2), 195-215. Https://doi.org/10.1108/TQM-02-2014-0026

Kiran, K., & Diljit, S. (2011). Antecedents of customer loyalty: Does service quality suffice?. Malaysian Journal of Library & Information Science, 16 (2), 95-113. http://mjs.um.edu.my/index.php/MJLIS/article/view/6701

Knemeyer, A. M., & Murphy, P. R. (2004). Evaluating the performance of third-party logistics arrangements: a relationship marketing perspective. Journal of supply chain management, 40 (4), 35-51. https://doi.org/10.1111/j.1745-493X.2004.tb00254.x

Kolodizieva, T., Zhelezniakova, E., Melnykova, K., Pysmak, V., & Kolodiziev, O. (2022). Assessment of logistics service quality based on the application of fuzzy methods modeling. http://dx.doi.org/10.21511/ppm.20(3).2022.44

Kotler, P., & Armstrong, G. (2006). Principles of marketing 11th edition Printice hall, USA

Kotler, P., Armstrong, G., Saunders, J., & Wong, V. (2002). Principles of Marketing. 3rd European Edition, London

Kulyk, P., Michalowska, M., & Kotylak, S. (2017). Assessment of customer satisfaction with logistics service in the light of the results of the research. Management, 21 (1), 205-222. https://doi.org/10.1515/manment-2015-0089

Kumar, V., & Pansari, A. (2014). The construct, measurement, and impact of employee engagement: A marketing perspective. Customer Needs and Solutions, 1, 52-67. https://doi.org/10.1007/s40547-013-0006-4

Kuruuzum, A., & Koksal, C. D. (2010). The impact of service quality on behavioral intention in hospitality industry. International journal of business and management studies, 2(1), 9-15. https://dergipark.org.tr/en/download/article-file/255972

Lages, C., Lages, C. R., & Lages, L. F. (2005). The RELQUAL scale: a measure of relationship quality in export market ventures. Journal of business research, 58 (8), 1040-1048. https://doi.org/10.1016/j.jbusres.2004.03.001

Langfield-Smith, K. (2008). The relations between transactional characteristics, trust and risk in the start-up phase of a collaborative alliance. Management Accounting Research, 19 (4), 344­364. https://doi.org/10.1016/j.mar.2008.09.001

Lawler, E. J., & Thye, S. R. (1999). Bringing emotions into social exchange theory. Annual review of sociology, 25 (1), 217-244. https://doi.org/10.1146/annurev.soc.25.1.217

Lee, H., Lee, Y., & Yoo, D. (2000). The determinants of perceived service quality and its relationship with satisfaction. Journal of services marketing, 14(3), 217-231. https://doi.org/10.1108/08876040010327220

Lei, Z., Duan, H., Zhang, L., Ergu, D., & Liu, F. (2022). The main influencing factors of customer satisfaction and loyalty in city express delivery. Frontiers in Psychology, 13, 1-9. https://doi.org/10.3389/fpsyg.2022.1044032

Leisen Pollack, B. (2017). Effects of exit barriers on word of mouth activities. Journal of Services Marketing, 31 (6), 512-526. https://doi.org/10.1108/JSM-01-2016-0024

Li, B., Riley, M. W., & Hsieh, C. T. (2004). Assessing customer satisfaction in parcel delivery industry: an empirical study among university customers. International Journal of Services and Standards, 1 (2), 172-192. https://doi.org/10.1504/IJSS.2004.005695

Li, X., Wen, L., & Xuan, Z. (2011). Service process optimization of China mobile multimedia broadcasting based on the gaps model of service quality and the service blueprint. International Conference on E-Business and E-Government (ICEE),1-4. https://doi.org/10.1109/ICEBEG.2011.5881593

Libo-on, J. T. (2021). Service quality influence on customer satisfaction in courier services: A comparative study. American International Journal of Business Management (AIJBM), 4 (3), 51­63. https://www.aijbm.com/wp-content/uploads/2021/04/F435163.pdf

Loyyl, B. W., & Kumar, M. (2018). A critical analysis on service quality and customer retention. http://dx.doi.org/10.2139/ssrn.3201409

Lusch, R. F., Vargo, S. L., & O’Brien, M. (2007). Competing through service: Insights from servicedominant logic. Journal of Retailing, 83(1), 5-18. https://doi.org/10.1016/j.jretai.2006.10.002

Maqbool, R., Noor, A., Fatima, K., Khalid, S., & Maqbool, N. (2020). CUSTOMERS TRUST PROPENSITY: MEDIATING EFFECT OF SALESPERSONS CHARACTERISTICS. International Journal of Management Research and Emerging Sciences, 10 (2). https://doi.org/10.56536/ijmres.v10i2.95

Mazzola, E., & Perrone, G. (2013). A strategic needs perspective on operations outsourcing and other inter-firm relationships. International Journal of Production Economics, 144 (1), 256-267. https://doi.org/10.1016/j.ijpe.2013.02.012

Mendez-Suarez, M. (2021). Marketing Mix Modeling Using PLS-SEM, Bootstrapping the Model Coefficients. Mathematics, 9 (15), 1832. https://doi.org/10.3390/math9151832

Mentzer, J. T., Flint, D. J., & Kent, J. L. (1999). Developing a logistics service quality scale. Journal of Business Logistics, 20(1), 9-32. https://api.semanticscholar.org/CorpusID:14469496

Mokhtary, Z., & Pazhouh, B. (2012). Measuring Service Quality Using Complement Methods for Gap Model. International Journal of Machine Learning and Computing, 2 (5), 640. https://www.ijml.org/papers/205-L30036.pdf

Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of marketing, 58 (3), 20-38. https://doi.org/10.1177/002224299405800302

Mulyono, H., & Fikri, M. H. (2023). Servicecape Analysis To Customer Satisfaction In Delivery Service Company With Customer Trust As Intervening Variable. Jurnal Manajemen Industri dan Logistik, 7(1), 01-10. http://doi.org/10.30988/jmil.v7i1.1023

Nautwima, J. P., & Asa, A. R. (2022). The impact of quality service on customer satisfaction in the banking sector amidst Covid-19 pandemic: A literature review for the state of current knowledge. International Journal of Management Science and Business Administration, 8 (3), 31­38. https://doi.org/10.18775/ijmsba.1849-5664-5419.2014.83.1004

Ndubisi, N. O. (2007). Relationship marketing and customer loyalty. Marketing intelligence & planning, 25 (1), 98-106. https://doi.org/10.1108/02634500710722425

Ngaliman, E., & Suharto, M. (2019). The Effect of Tangibles, Responsiveness, and Reliability on Customer Satisfaction of Delivery Services. International Journal of Economics and Management Studies, 6(5), 86-92. https://doi.org/10.14445/23939125/IJEMS-V6I5P113

Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of marketing research, 17 (4), 460-469. https://doi.org/10.1177/002224378001700405

Oraedu, C., Izogo, E. E., Nnabuko, J., & Ogba, I. E. (2021). Understanding electronic and face- to-face word-of-mouth influencers: an emerging market perspective. Management Research Review, 44 (1), 112-132. https://doi.org/10.1108/MRR-02-2020-0066

Osman, A. R., Sohel-Uz-Zaman, A. S. M., Ashraf, M. A., & Uddin, A. (2020). Vindicating Service Quality of Education through Structural Equation Modeling (SEM): International Students' Perspective. International Journal of Higher Education, 9 (3), 158-172. https://doi.org/10.5430/ijhe.v9n3p158

Ostrom, A. L., Parasuraman, A., Bowen, D. E., Patricio, L., & Voss, C. A. (2015). Service research priorities in a rapidly changing context. Journal of service research, 18 (2), 127-159. https://doi.org/10.1177/1094670515576315

Palvia, P. C., King, R. C., Xia, W., & Palvia, S. C. J. (2010). Capability, quality, and performance of offshore IS vendors: A theoretical framework and empirical investigation. Decision sciences, 41 (2), 231-270. https://doi.org/10.1111/j.1540-5915.2010.00268.x

Pantano, E., Pizzi, G., Scarpi, D., & Dennis, C. (2020). Competing during a pandemic? Retailers’ ups and downs during the COVID-19 outbreak. Journal of Business research, 116, 209-213. https://doi.org/10.1016/j.jbusres.2020.05.036

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A Conceptual Model of Service Quality and Its Implications for Future Research. Journal of Marketing, 49 (4), 41-50. https://doi.org/10.1177/002224298504900403

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1994). Reassessment of expectations as a comparison standard in measuring service quality: implications for further research. Journal of marketing, 58 (1), 111-124

Parasuraman, A., Zeithaml, V., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64, 12-40

Parsons, A. L. (2002). What determines buyer-seller relationship quality? An investigation from the buyer's perspective. Journal of Supply Chain Management, 38 (1), 4-12. https://doi.org/10.1111/j.1745-493X.2002.tb00124.x

Purwanto, A., & Sudargini, Y. (2021). Partial Least Squares Structural Squation Modeling (PLS- SEM) Analysis for Social and Management Research: A Literature Review. Journal of Industrial Engineering & Management Research, 2 (4), 114- 123. https://doi.org/10.7777/jiemar.v2i2

Purwianti, L., & Khoviati, L. S. (2021). ANALYSIS EFFECT OF SERVICE RECOVERY ON CUSTOMER LOYALTY WITH SATISFACTION AND WOM (WORD OF MOUTH) AS MEDIATION VARIABLES AT FOUR-STAR HOTELS IN BATAM CITY. Journal of Business Studies and Management Review, 4 (2), 154-158. https://doi.org/10.22437/jbsmr.v4i2.12524

Queirós, A., Faria, D., & Almeida, F. (2017). Strengths and limitations of qualitative and quantitative research methods. European journal of education studies, 3 (9), 369-387. http://dx.doi.org/10.5281/zenodo.887089

Querin, F., & Göbl, M. (2017). An analysis on the impact of Logistics on Customer Service.

Journal of Applied Leadership and Management, 5, 90-103. https://hdl.handle.net/10419/175337

Raajpoot, N. (2004). Reconceptualizing service encounter quality in non-western context.

Journal of Service Research, 7(2), 181-201. https://doi.org/10.1177/1094670504268450

Rao, P. S., & Sahu, P. C. (2013). Impact of service quality on customer satisfaction in hotel industry. IOSR Journal of Humanities and Social Science, 18 (5), 39-44. http://dx.doi.org/10.9790/0837-1853944

Reichheld, F. F., & Sasser, W. E. (1990). Zero defections: quality comes to services. 1990, 68 (5), 105-111

Rigopoulou, I. D., Tsiotsou, R. H., & Kehagias, J. D. (2008). Shopping orientation-Defined segments based store-choice criteria and satisfaction: an empirical investigation. Journal of Marketing Management. 24 (9-10), 979-995. https://doi.org/10.1362/026725708X382000

Ringle, C. M., & Spreen, F. (2007). Beurteilung der Ergebnisse von PLS-Pfadanalysen. Das Wirtschaftsstudium, 36 (2), 211-216

Ringle, C. M., Wende, S., & Becker, J.-M. (2024). "SmartPLS 4." Bönningstedt: SmartPLS, https://www.smartpls.com

Rowley, J. (2005). The four Cs of customer loyalty. Marketing intelligence & planning, 23 (6), 574-581. https://doi.org/10.1108/02634500510624138

Roxenhall, T., & Andresen, E. (2012). Affective, calculative and normative commitment: An assesment of relationship. World Review of Business Research, 2 (5), 86-96. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-17190

Ryglova, K. (2011). The marketing tools of quality management in tourism services. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 59 (2), 257-266. http://dx.doi.org/10.11118/actaun201159020257

Sambasivan, M., Siew-Phaik, L., Abidin Mohamed, Z., & Choy Leong, Y. (2011). Impact of interdependence between supply chain partners on strategic alliance outcomes: role of relational capital as a mediating construct. Management Decision, 49 (4), 548-569. https://doi.org/10.1108/00251741111126486

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial Least Squares Structural Equation Modeling. In C. Homburg, M. Klarmann, & A. E. Vomberg (Eds.), Handbook of Market Research, 1-47. Springer International Publishing. https://doi.org/10.1007/978-3-319-05542-8_15-2

Saunders, M. N. K., Lewis, P., & Thornhill, A. (2019). Research methods for business students(Eighth edition). Pearson Education

Schröder, F., & Brandenburg, (2019). Ethische Fragen beim Einsatz quantitativer Methoden in der Erforschung und Gestaltung von Mensch-Technik-Interaktion. https://doi.org/10.18420/muc2019-ws-258-01

Sharma, N., Young, L., & Wilkinson, I. (2006). The commitment mix: Dimensions of commitment in international trading relationships in India. Journal of International Marketing, 14 (3), 64-91. https://doi.org/10.1509/jimk.14.3.64

Shukri, S. M., Yajid, M. S. A., & Tham, J. (2020). Role of responsiveness, reliability and tangibility on customer satisfaction. A Multifaceted Review Journal in the Field of Pharmacy, 11(1), 1007-1013. https://dx.doi.org/10.5530/srp.2020.1.127

Siali, F., Wen, A. W. S., & Hajazi, M. U. A. (2018). Booming of online shopping in Malaysia: Do customers satisfy with parcel delivery service. International Journal of Academic Research in Business and Social Sciences, 8(12), 415-436. http://dx.doi.org/10.6007/IJARBSS/v8-i12/5042

Siddiqui, M. S., Siddiqui, U. A., Khan, M. A., Alkandi, I. G., Saxena, A. K., & Siddiqui, J. H. (2021). Creating Electronic Word of Mouth Credibility through Social Networking Sites and Determining Its Impact on Brand Image and Online Purchase Intentions in India. Journal of Theoretical and Applied Electronic Commerce Research, 16 (4), 1008-1024. https://doi.org/10.3390/jtaer16040057

Simanjuntak, M., Putri, N. E., Yuliati, L. N., & Sabri, M. F. (2020). Enhancing customer retention using customer relationship management approach in the car loan business. Cogent Business & Management, 7(1). https://doi.org/10.1080/23311975.2020.1738200

Sower, V., Duffy, J. A., Kilbourne, W., Kohers, G., & Jones, P. (2001). The dimensions of service quality for hospitals: development and use of the KQCAH scale. Health Care Management Review, 26(2), 47-58. https://doi.org/10.1097/00004010-200104000-00005

Spiro, R. L., & Weitz, B. A. (1990). Adaptive selling: Conceptualization, measurement, and nomological validity. Journal of marketing Research, 27 (1), 61-69. https://doi.org/10.1177/002224379002700106

Stank, T. P., Daugherty, P. J., & Ellinger, A. E. (1998). Pulling customers closer through logistics services. Business Horizons, 41(5), 74-81. https://link.gale.com/apps/doc/A21186700/AONE?u=anon~9adf3504&sid=googleScholar&xid=ff dd1353

Statista, (2022a). Retail e-commerce revenue in Austria from 2017 to 2025. https://www.statista.com/forecasts/1342705/austria-e-commerce-revenue

Statista, (2022b). Online purchases by category in Austria in 2022. https://www.statista.com/forecasts/1001224/online-purchases-by-category-in-austria

Statista, (2022c). Drivers of online purchases in Austria in 2022. https://www.statista.com/forecasts/1001321/drivers-of-online-purchases-in-austria

Statista, (2023). Anteil der Online-Käufer in Österreich nach Alter im Jahr 2023. https://de.statista.com/statistik/daten/studie/299801/umfrage/online-shopper-in-oesterreich-nach- alter/

Statistik Austria (2024). Bevölkerung nach Alter/Geschlecht. https://www.statistik.at/statistiken/bevoelkerung-und-soziales/bevoelkerung/bevoelkerungsstand/bevoelkerung-nach-alter/geschlecht

Sujan, H. (1999). Optimism and street-smarts: Identifying and improving salesperson intelligence. Journal of Personal Selling & Sales Management, 19 (3), 17-33. https://doi.org/10.1080/08853134.1999.10754179

Sumaedi, S., Bakti, I. G. M. Y., & Yarmen, M. (2012). The empirical study of public transport passengers’ behavioral intentions: The roles of service quality, perceived sacrifice, perceived value, and satisfaction. International journal for traffic and transport engineering, 2 (1), 83-97. http://www.ijtte.com/uploads/2012-03-20/d4c8811d-88a5-92b1p8.pdf

Sumi, R. S., & Kabir, G. (2021). Satisfaction of e-learners with electronic learning service quality using the servqual model. Journal of Open Innovation: Technology, Market, and Complexity, 7(4), 227, 1-17, 7. https://doi.org/10.3390/joitmc7040227

Sureshchandar, G. S., Rajendran, C., & Anantharaman, R. N. (2002). The relationship between service quality and customer satisfaction-a factor specific approach. Journal of services marketing, 336-379. https://doi.org/10.1108/08876040210433248

Susilowati, Y., & Yasri, Y. (2019). The effect of service quality and customer satisfaction toward word of mouth intention. 2nd Padang International Conference on Education, Economics, Business and Accounting (PICEEBA-2 2018), 393-401. https://doi.org/10.2991/piceeba2- 18.2019.52

Sutrisno, A., Andajani, E., & Widjaja, F. N. (2019). The effects of service quality on customer satisfaction and loyalty in a logistics company. KnE Social Sciences, 85-92. https://doi.org/10.18502/kss.v3i26.5360

Sweeney, J., & Swait, J. (2008). The effects of brand credibility on customer loyalty. Journal of retailing and consumer services, 15 (3), 179-193. https://doi.org/10.1016/j.jretconser.2007.04.001

Sweeney, J., Payne, A., Frow, P., & Liu, D. (2020). Customer advocacy: A distinctive form of word of mouth. Journal of Service Research, 23 (2), 139-155. https://doi.org/10.1177/1094670519900541

Tang, R. Q., Tan, Y. J., Tan, Z. X., Tan, Y. T., Almawad, G., & Alosaimi, A. (2022). A study of courier service quality and customer satisfaction. International Journal of Applied Business and International Management (IJABIM), 7 (1), 137-150. https://doi.org/10.32535/ijabim.v6i3.1446

Taran, A. M. (2022). Evaluating the Level of the Shuttle Service Quality at Al al-Bayt University Campus Using the SERVQUAL Model. Planning, 17 (6), 1905-1915. https://doi.org/10.18280/ijsdp.170625

Teas, R. K. (1993). Expectations, performance evaluation, and consumers’ perceptions of quality. Journal of marketing, 57 (4), 18-34. https://doi.org/10.1177/002224299305700402

Terho, H., Haas, A., Eggert, A., & Ulaga, W. (2012). ‘It's almost like taking the sales out of selling’—Towards a conceptualization of value-based selling in business markets. Industrial Marketing Management, 41 (1), 174-185. https://doi.org/10.1016/j.indmarman.2011.11.011.

Tian, Y., Lai, F., & Daniel, F. (2008). An examination of the nature of trust in logistics outsourcing relationship: empirical evidence from China. Industrial Management & Data Systems, 108 (3), 346-367. https://doi.org/10.1108/02635570810858769

Timothy Coombs, W., & Holladay, S. J. (2007). The negative communication dynamic: Exploring the impact of stakeholder affect on behavioral intentions. Journal of Communication management, 11 (4), 300-312. https://doi.org/10.1108/13632540710843913

Too, L. H., Souchon, A. L., & Thirkell, P. C. (2001). Relationship marketing and customer loyalty in a retail setting: a dyadic exploration. Journal of Marketing Management, 17 (3-4), 287-319. https://doi.org/10.1362/0267257012652140

Tsoukatos, E., & Rand, G. K. (2006). Path analysis of perceived service quality, satisfaction and loyalty in Greek insurance. Managing Service Quality: An International Journal, 16 (5), 501-519. https://doi.org/10.1108/09604520610686746

Uzir, M. U. H., Al Halbusi, H., Thurasamy, R., Hock, R. L. T., Aljaberi, M. A., Hasan, N., & Hamid, M. (2021). The effects of service quality, perceived value and trust in home delivery service personnel on customer satisfaction: Evidence from a developing country. Journal of Retailing and Consumer Services, 63, 102721, 1-15. https://doi.org/10.1016/j.jretconser.2021.102721

Vargo, S. L., & Lusch, R. F. (2014). Evolving to a new dominant logic for marketing. The service­dominant logic of marketing, 21-46. https://doi.org/10.1509/jmkg.68.1.1.24036

Viswanadham, N., Sharma, S. M., & Taneja, M. (1996). Inspection allocation in manufacturing systems using stochastic search techniques. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 26 (2), 222-230. https://doi.org/10.1109/3468.485748

Voon, B. H., Kueh, K. M. K., Unggah, L., & Chali, R. (2009). Customers’ perception of restaurant service quality: evidence from Malaysia. Social and Management Research Journal (SMRJ), 6 (1), 33-50. https://doi.org/10.24191/smrj.v6i1.5167

Wang, F., & Hu, Y. (2016). Research on certification standards of LSQ. American Journal of Industrial and Business Management, 6(8), 895-899. https://doi.org/10.4236/ajibm.2016.68086

Wang, X. (2011). The effect of inconsistent word-of-mouth during the service encounter. Journal of Service Marketing, 25 (4), 252-259. https://doi.org/10.1108/08876041111143087

Wilder, K. M., Collier, J. E., & Barnes, D. C. (2014). Tailoring to customers’ needs: Understanding how to promote an adaptive service experience with frontline employees. Journal of Service Research, 17 (4), 446-459. https://doi.org/10.1177/1094670514530043

Wilkins, H., Merrilees, B., & Herington, C. (2007). Toward an understanding of total service quality in hotels. International Journal of Hospitality Management, 26( 4), 840-53. https://doi.org/10.1016/j.ijhm.2006.07.006

Williams, K. C., & Spiro, R. L. (1985). Communication style in the salesperson-customer dyad. Journal of marketing Research, 22 (4), 434-442. https://doi.org/10.1177/002224378502200408

Wilson, A., Zeithaml, V., Bitner, M. J., & Gremler, D. (2016). EBOOK: Services Marketing: Integrating customer focus across the firm. McGraw-Hill

Yang, J. (2024). Research on After-sales Problems in the Express Delivery Industry. 2023 5th International Conference on Economic Management and Cultural Industry (ICEMCI 2023), 707­714. Atlantis Press. https://doi.org/10.2991/978-94-6463-368-9_84

Yap, S. F., & Kew, M. L. (2007). Service quality and customer satisfaction: antecedents of customer's re-patronage intentions. Sunway academic journal, 4, 59-73. http://eprints.sunway.edu.my/id/eprint/46

Yasri, Y., & Engriani, Y. (2018). An Analysis on Brand Image and Switching Intention of Flight Companies’ Customers with Word of Mouth (WOM) as The Intervening Variable. First Padang International Conference On Economics Education, Economics, Business and Management, Accounting and Entrepreneurship (PICEEBA 2018), 347-358. https://doi.org/10.2991/piceeba- 18.2018.66

Yee, H. L., & Daud, D. (2011). Measuring customer satisfaction in the parcel service delivery: a pilot study in Malaysia. Business and Economic Research, 1(1), 1-10. https://doi.org/10.5296/ber.v1i1.1125

Yee, R. W., Lee, P. K., Yeung, A. C., & Cheng, T. E. (2013). The relationships among leadership, goal orientation, and service quality in high-contact service industries: An empirical study. International Journal of Production Economics, 141 (2), 452-464. https://doi.org/10.1016/j.ijpe.2011.12.012

Yu, B., Zhang, S., Wu, S., & Xie, J. (2013). A Study of Courier Service Quality Improvement Based on a Two-Stage QFD. Journal of System and Management Sciences, 3(3), 26-33. https://link.springer.com/book/10.1007/978-3-642-32054-5

Zaid, S., Palilati, A., Madjid, R., Juharsah, & Suleman, N., R. (2021). Moderating and Mediating Role of Trust on Relationship between Servqual andSatisfaction in Delivery Service. Journal of Economics, Finance and Management Studies, 4(6), 837-843. https://doi.org/10.47191/jefms/v4- i6-20

Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1988). Communication and control processes in the delivery of service quality. Journal of marketing, 52 (2), 35-48. https://doi.org/10.1177/002224298805200203

Zineldin, M. (2005). Quality and customer relationship management (CRM) as competitive strategy in the Swedish banking industry. The TQM Magazine, 17(4), 329-344. https://doi.org/10.1108/09544780310487749

Zoltners, A. A., Sinha, P., & Lorimer, S. E. (2008). Sales force effectiveness: a framework for researchers and practitioners. Journal of Personal Selling & Sales Management, 28 (2), 115-131. https://doi.org/10.2753/PSS0885-3134280201

Appendix

Fragebogen

Liebe Teilnehmer*innen,

vielen Dank, dass Sie sich ein wenig Zeit nehmen (15 Minuten), um an dieser Umfrage teilzunehmen.

Im Rahmen meiner Masterarbeit im Studiengang Management am „Institut für Handel, Absatz und Marketing an der Johannes-Kepler-Universität Linz“ führe ich derzeit eine Befragung zum Thema „Kund*innenzufriedenheit mit Paketzusteller*innen “ durch.

Die Befragung erfolgt anonym.

Ich freue mich über Ihre Teilnahme und bedanke mich recht herzlich für Ihre Unterstützung!

Mit lieben Grüßen

Demographie 1

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Einkaufsverhalten 1:

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Einkaufsverhalten 2:

Durchschnittliche Ausgaben beim Online-Shopping

Q08: Wie viele Euro geben Sie durchschnittlich pro Einkauf aus, wenn Sie sich Waren liefern lassen?

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Erfahrung

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Produktkategorien

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Lieferung von Paketen

Bitte beurteilen Sie die Zustelloption „Lieferung“ anhand folgender Fragen:

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Bitte beurteilen Sie Ihre weiteren Kaufabsichten nach der Erfahrung mit dem Zustelldienst. Bitte pro Zeile eine Antwort.

Die Wahrscheinlichkeit, dass ich mir in der Zukunft Waren liefern lasse, ist:

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Bitte beurteilen Sie Ihre Absichten den Zustelldienst Ihren Freund*innen/Bekannten weiterzuempfehlen.

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Zufriedenheit / Loyalität

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Denken Sie an Zufriedenheit mit dem Zustelldienst. Inwieweit stimmen sie den folgenden Aussagen zu:

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Loyalität

Inwieweit stimmen sie den folgenden Aussagen zu:

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Bereitschaft den Zustelldienst zu wechseln:

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Bereitschaft den Zustelldienst beizubehalten:

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Checks:

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Bitte geben Sie Ihre persönliche Meinung an

Inwieweit stimmen Sie der folgenden Aussage zu oder nicht?

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SERVQUAL:

Zur Beantwortung der folgenden Fragen (Q37 - Q80) verwenden Sie bitte die nachstehende 7­stufige Likert-Skala.

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Shopper Typs:

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Demographie 2

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Vielen Dank, dass Sie sich für meine Befragung Zeit genommen haben!

Herzliche Grüße

References to the questionnaire items

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[...]

• College: University of Linz (Institute for Retailing, Sales and Marketing)
• Grade: 1,0
• Author: Anonym (Author)
• Publication Year: 2025
• Pages: 103
• Catalog Number: V1595635
• ISBN (PDF): 9783389144169
• ISBN (Book): 9783389144176
• Language: English
• Tags: SERVQUAL, customer satisfaction, parcel delivery, courier service, loyalty, trust, word of mouth.