This thesis comparatively investigates factors for customers satisfaction in voice commerce and e-commerce to assess the emphasis customers place on factors in both channels. Voice commerce is a newly evolving electronic commerce channel where customers communicate with dedicated systems on smart speakers, mobile phones or other devices using their voice, in order to find and order products.
This thesis identifies customer satisfaction predictors that potentially differ between both channels: convenience and transaction process efficiency are based on previous research on chatbot and digital assistant expectations. In the area of recommendations, recommendation personalization (the degree of personalization of product recommendations) is identified from previous research. The construct of recommendation complexity has been created, which is the degree of detail and amount of information recommendations are presented. Differences in this domain of computer-human-interaction are explained by media richness theory, an application of neuro-ergonomics.
Data was collected through a survey conducted on the crowdsourcing platform Amazon MTurk. The sample consisted of 178 US consumers that had purchased goods using both e-commerce and voice commerce. Structural equation modeling (SEM) was used as well as multiple regression analysis for statistical hypotheses testing. Two SEM models were created for each voice commerce and e-commerce and both models were compared to investigate comparative hypotheses.
This research enables product managers to recognize which factors of customers satisfaction differ from those in e-commerce. While developing their voice commerce strategy and system design, managers should emphasize convenience factors such as ease of use and ease of understanding, as well as an efficient transaction process.
Contents
1 Introduction
1.1 Background
1.2 Outline and Scope
2 Literature Review and Hypotheses Development
2.1 Overview
2.2 Voice Commerce
2.3 Brief History of the Conversational Interface
2.4 Differences in Electronic Commerce Channels
2.4.1 Comparison Framework
2.4.2 Technology Dimension
2.4.3 Value Dimension
2.4.4 Media Richness
2.5 Voice Unimodality
2.5.1 Speech Input
2.5.2 Speech Output
2.6 Customer Satisfaction Factors
2.6.1 Recommendation Complexity
2.6.2 Recommendation Personalization
2.6.3 Convenience
2.6.4 Transaction Process Efficiency
3 Research Methodology
3.1 Study Design and Measurement Scales
3.2 Data Collection
3.2.1 Acquisition using MTurk
3.2.2 Screening Survey
3.2.3 Main Survey
3.2.4 Quality Control
3.3 Respondent Characteristics
3.3.1 Age and Gender
3.3.2 IT Affinity
3.3.3 Usage Frequency and Technologies
4 Data Analysis
4.1 Sample Size Considerations
4.2 Descriptive Statistics
4.3 Reliability
4.4 Principal Component Analysis
4.5 Structural Equation Modeling
4.5.1 Measurement Models
4.5.2 Structural Models
4.5.3 Empirical Findings
4.6 Moderation
4.7 Multiple Regression Analysis
5 Considerations and Recommendations
5.1 Discussion
5.2 Implications for Practice
5.3 Implications for Research
5.4 Limitations and Design Issues
5.5 Directions for Future Research
Research Objectives and Themes
The primary objective of this thesis is to identify and compare factors influencing customer satisfaction in B2C e-commerce and voice commerce environments, assessing how these factors differ based on the communication channel.
- Comparison of customer satisfaction predictors between e-commerce and voice commerce.
- Analysis of convenience, transaction process efficiency, recommendation complexity, and personalization.
- Investigation into the influence of media richness on channel perception.
- Empirical validation using Structural Equation Modeling (SEM) based on data from US consumers.
- Development of strategic design recommendations for product managers in voice commerce.
Excerpt from the Book
1.1 Background
Since their introduction in 2014, the use of intelligent virtual assistants based on smart speakers like Amazon Alexa, Apple HomePod, Microsoft Cortana and Google Home is increasing (Broder, 2018). Moar (2017) estimates that there are currently 450 million voice assistant devices in the US, expected to reach 870 million by 2020. These systems make it possible to conduct a “zero-click” purchase in business to consumer (B2C) commerce scenarios. Communicating with the assistant using only their voice, customers can formulate search queries and confirm purchase actions without the need to use common visual or typing interfaces. E-commerce experts label this scenario "voice commerce" and expect it to be one of the most important innovations to shape the next years of e-commerce development (McTear, 2017; Luger and Sellen, 2016; Chopra and Chivukula, 2017). These systems involve natural language processing (NLP), intent recognition, speech synthesis, recommender systems and artificial intelligence (AI) technologies (Brandtzaeg, 2017; Luger and Sellen, 2016).
There has been a long-standing research interest in customer satisfaction and loyalty factors for e-commerce applications (Yang, 2015; Colla and Lapoule, 2012; Srinivasan et al., 2002; Fuentes-Blasco et al., 2010). Similar research on e-commerce via mobile devices (m-commerce) is also present (Ngai and Gunasekaran, 2007; San Martín et al., 2012; Sohn et al., 2017; Wang and Liao, 2007; Li and Yeh, 2010; Lin and Wang, 2006; Marinkovic and Kalinic, 2017). So is research on differences of m-commerce and e-commerce and respective customer satisfaction factors (CSF) (Cao et al., 2015; Maity and Dass, 2014; Choi et al., 2008), on commerce using conversational text-based interfaces (AbuShawar and Atwell, 2016; Peng et al., 2016; Hostler et al., 2005; Ben Mimoun et al., 2017; Mahmood et al., 2014) and on recommender systems (Li and Karahanna, 2015; Mahmood et al., 2014; Liang et al., 2006). Specific research on e-commerce in a human-to-AI voice-based scenario is, however, sparse.
Summary of Chapters
1 Introduction: Provides the background and research motivation regarding the rise of voice commerce and outlines the scope of the comparative study.
2 Literature Review and Hypotheses Development: Reviews existing theories on e-commerce, chatbots, and media richness to develop a model and twelve hypotheses regarding customer satisfaction factors.
3 Research Methodology: Describes the study design, measurement scales, data acquisition via MTurk, and quality control procedures used for the empirical validation.
4 Data Analysis: Presents the statistical evaluation, including descriptive statistics, reliability tests, structural equation modeling results, and moderation analysis.
5 Considerations and Recommendations: Discusses the findings, provides practical implications for product design, notes limitations, and suggests directions for future academic research.
Keywords
Voice Commerce, E-Commerce, Chatbots, Recommender Systems, Customer Satisfaction, Media Richness, User Experience, Structural Equation Modeling, Conversational Interfaces, Transaction Efficiency, Personalization, Digital Assistants, Consumer Behavior, Human-Computer Interaction, Amazon MTurk.
Frequently Asked Questions
What is the fundamental focus of this research?
This research investigates the factors influencing customer satisfaction in B2C voice commerce and compares them to those in traditional e-commerce to determine where the drivers for user satisfaction differ.
What are the central thematic areas covered in the study?
The study centers on customer satisfaction factors, specifically convenience, transaction process efficiency, recommendation complexity, and recommendation personalization, analyzed through the lens of media richness theory.
What is the primary research goal?
The goal is to answer the question: "How do factors influencing customer satisfaction in B2C voice commerce differ from those in e-commerce?" and to provide actionable insights for developers.
Which scientific method was applied?
The author employed a quantitative approach using a self-administered survey of 178 US consumers, analyzing the data through structural equation modeling (SEM) and multiple regression analysis.
What topics are discussed in the main body?
The main body covers a literature review of commerce channels, the theoretical development of hypotheses, the methodology of the empirical study, and the subsequent statistical data analysis.
What characterize this work in terms of keywords?
The work is characterized by terms such as Voice Commerce, E-Commerce, Customer Satisfaction, Media Richness, and Conversational Interfaces.
How does convenience impact voice commerce differently than e-commerce?
The findings indicate that convenience has a significantly larger effect on customer satisfaction in voice commerce compared to e-commerce, driven by the higher ease-of-use expectations of the voice interface.
Why did the author use MTurk for data collection?
Amazon MTurk was used to capture a broader, more diverse US population with higher technology affinity, facilitating the acquisition of a sufficient sample size for empirical validation under research constraints.
What practical advice does the thesis offer to managers?
Managers are advised to emphasize "hands-free" convenience, minimize the number of steps in the transaction process, and consider adding visual components when designing for mobile-based voice commerce.
- Citation du texte
- Daniel Kraus (Auteur), 2018, Factors Influencing Customer Satisfaction. Differences Between E-Commerce and Voice Commerce, Munich, GRIN Verlag, https://www.grin.com/document/463605
