The development of Information Systems and emerging business models


Term Paper (Advanced seminar), 2021

10 Pages, Grade: 2.7


Excerpt

THE DEVELOPMENT OF INFORMATION SYSTEMS AND EMERGING BUSINESS MODELS

Research Paper

Abstract

Disruptive technologies have fundamentally changed the way profits are created and therefore the business models of both startups and legacy companies. The transformation of information systems, as a research discipline dedicated to this topic, will be discussed, to then address current research on digital business models. Based on the most promising technologies of the next years, emerging business models will be presented.

Keywords: Information Systems, Business Models, Information Communication Technology, Innovation.

1 Introduction

It's nothing new that business models are changing due to new technologies, but nowadays it's not just about selling your goods online, but about completely new business models. How is it that some companies become successful with their digital business models and others fail, and what factors can or must be considered. This is one of the tasks of the information systems discipline. However, since the researcher have different points of view that evolve over time, and since information systems must repeatedly justify itself as a discipline, first an overview of its development will be provided. Then the technologies from the Gartner Hype Cycle with the highest expectations will be presented, such as artificial intelligence, which can itself create artifacts such as source code or design. To better analyze the potential value creation opportunities that can arise from these technologies, the basics of business models will first be presented and in particular how business models must be adapted to the new digital possibilities.

2 Information Systems

First, it must be clarified what the term information systems (IS) means exactly. Since information systems has become a field of great interest, there is a lot of literature on the subject with different definitions.

Boell and Cecez-Kecmanovic (2015, p. 4959) state that IS “involve a variety of information technologies (IT) such as computers, software, databases, communication systems, the Internet, mobile devices and much more, to perform specific tasks, interact with and inform various actors in different organizational or social contexts”. They also emphasize that IS must be seen in the context of societies or organizations and that it is not about the technical implementation per se, but how information demands in the different contexts can be met by technologies. Rainer and Prince (2021) place less emphasis on the sociotechnical component and describe IS as “getting the right information to the right people, at the right time. in the right amount, and in the right format”. Stair and Reynolds (2020) stress the business aspect of IS by highlighting the benefits of IS for operational tasks as well as for data-based decision-making. According to Peffers, Tuunanen, Rothenberger, and Chatterjee (2007), IS is an interdisciplinary research discipline that attempts to mediate between business administration, computer science, and social science.

Although there is some overlap in the definitions of IS, differences exist precisely in the balance between the business and technology components. Therefore, the following chapter will give an overview of the development of IS, both in research and in business, and elaborate on its different manifestations. Before that, some important definitions are provided, which are important for further understanding.

Information technology (IT) is a general term used to describe all computer-based systems for information processing (Rainer and Prince, 2021). Information Communication Technology (ICT) can be seen as a part of IT and focuses on the transmission of information (UNESCO Institute of Statistics, n.d.). Rainer and Prince (2021) define data as electronically stored items that are stored in their most elementary form but are not sorted. According to them, data becomes information when it is stored and sorted in such a way that meaning can be recognized. When this information is connected in such a way that something can be deduced from it, it is called knowledge (Rainer and Prince, 2021).

2.1 Development

Information Systems was not always as accepted as a separate discipline as it is today. Hirschheim and Klein (2012) date the emergence of IS to the 1960s when IBM introduced the 360 series of computers for companies on which programs for different systems could be programmed. The focus quickly changed from data control systems in the early sixties to sociotechnical systems in the late seventies. With the increased availability of personal computers to more employees due to falling prices, the use of the technology was quickly recognized by IS as an economic advantage, which was increasingly reinforced by the introduction of the Internet (Hirschheim & Klein, 2012). This included in particular the globalization and outsourcing of tasks made possible by the Internet. Hirschheim and Klein (2012) note that criticism of the IS has existed from the beginning and continues. One of the most famous critiques of IS is certainly the article "IT doesn't matter" by Carr (2003). His main criticism is that the use of IT for corporate purposes has become so widespread that there is no strategic advantage as all competitors are doing the same, which also calls into question the very existence of IS. He also points out the risks that can arise through the use of IT, for example through software errors, and that spending on IT must be chosen more consciously (Carr, 2003; Pearlson & Saunders, 2013). However, Carr also earned a lot of criticism for his critical approach. Brown and Hagel (2003), for example, point out that IS has not reached an end state and continues to promote technical innovations and that its use benefits the individual company first and not the industry as a whole.

In the meantime, two streams of information systems have emerged. A behavior-oriented approach, which calls for more rigorous research, and a design-oriented approach, which focuses more on the technical development of IS systems (Robra-Bissantz & Strahringer, 2020). The application-oriented approach is criticized for not being scientific enough and is therefore denied its legitimacy as a research discipline by some critics (Gregor, 2002). The design-oriented approach, on the other hand, is accused of lacking practical relevance (Robra-Bissantz & Strahringer, 2020). As a result, a combination of both approaches has been formed, the Design Science Research (DSR), which originates from the Anglo-Saxon area and tries to combine the scientific and the practical approach (Iivari, 2015; Robra-Bissantz & Strahringer, 2020).

2.2 Benefits of implementing IS

The use of information communication technology for information systems purposes has various advantages. Hemmatfar, Salehi, and Bayat (2010) highlight the strategic advantage that can be achieved through IS in several ways. According to them, IS enables innovative applications that would not be possible without ICT or increase the efficiency of processes. In addition, customers and business partners can be better retained and communication can be made much easier (Hemmatfar, Salehi, & Bayat, 2010). In combination, these advantages also lead to cost reductions through more efficient processes (Pearlson & Saunders, 2013; Hemmatfar, Salehi, & Bayat, 2010). In addition, by using IS systems such as business intelligence, data can be collected and effectively used for business decisions.

However, the benefits that arise from the use of IS cannot always be directly identified and quantified. Therefore, Brown (1994) proposes to divide the benefits into hard and soft benefits, where hard benefits are those that are immediately identifiable and quantifiable, such as reduced personnel costs. The soft benefits, on the other hand, are not directly measurable and are more strategic (Brown, 1994). Giaglis, Mylonopoulos, and Doukidis (1999) have extended this theory and converted it into a model.

Abbildung in dieser Leseprobe nicht enthalten

Figure 1. Different types of IS benefits (Giaglis, Mylonopoulos, & Doukidis, 1999, p. 52)

Figure 1 (Giaglis, Mylonopoulos, & Doukidis, 1999, p. 52) shows this model and the relationships between measurability (horizontal axis) and attributability to IS (vertical axis). In addition to the hard benefits strongly associated with IS and easily measurable, there are three other dimensions of soft benefits. Giaglis, Mylonopoulos, and Doukidis (1999) speak of indirect benefits when the benefits are measurable but not directly attributable to IS, for example, because the implementation of one IS component only enables the implementation of another. On the contrary, they speak of intangibles when the IS system is the clear driver of improvement, but the benefits are not easily quantifiable, as in the case of decision systems. Strategic benefits, due to their long-term character and the interaction of many components, can neither be clearly assigned to IS nor well quantified (Giaglis, Mylonopoulos, & Doukidis, 1999).

However, since decision-makers need to be convinced of the cost investment for IS applications to achieve long-term strategic benefits, it is necessary to find methods to attribute the strategic benefits to the use of IS. Giaglis, Mylonopoulos, and Doukidis (1999) propose to measure benefits incrementally and thus to infer the soft benefits, especially the strategic ones, from the hard benefits. According to them, business process modeling (BPM) and simulation methods can be used for this purpose. Another model, the DeLone and McLean (D&M) model, for measuring the success of IS relies on the measurement of values such as system quality, user satisfaction, or net benefits, whereby interactions exist between the dimensions (Petter, DeLone, & McLean, 2008).

2.3 Current technology trends in the IS sector

However, a one-time investment in IS systems is not enough. To maintain IS as a strategic advantage, new trends and innovations in the IS sector must be observed and meaningfully integrated into corporate processes, as well as employees trained, and the necessary IT infrastructure kept up to date. Due to start-ups that are known to be innovation drivers and to bring disruptive technologies to the market quickly (Schaltegger & Wagner, 2011), the number of opportunities that the IS Department must evaluate is high. Keeping in mind Moore’s law that computer chips, due to halving prices, are equipped with twice as many transistors approximately every two years (Schaller, 1997), investments must also be made in IT hardware to support new computationally expensive technologies. The most promising technology trends are published annually by the IT research and consulting company Gartner.

Abbildung in dieser Leseprobe nicht enthalten

Figure 2. Hype Cycle for Emerging Technologies (Gartner, 2021)

Figure 2 shows this year's Hype Cycle for Emerging technologies (Gartner, 2021). The horizontal axis shows the phase the technology is currently in, and the vertical axis shows the expectation of the technology. The expected duration until the technology can be used productively is also indicated. Since this analysis is for technologies that are just emerging, they are all still in the "Innovation Trigger" or "Peak of Inflated Expectations" phase. Judging by the number and level of expectations, trends can be seen in the areas of Quantum based Machine Learning (ML) and Artificial Intelligence (AI) and technical trust. While AI and ML are not new technologies in themselves, new opportunities are created through the use of quantum computing. AI should not only be enriched with data, but these should be identified automatically, and ML and AI should be extended in such a way that they can create designs or software by themselves, which could increase quality and reduce costs (Ostler, 2021). Gartner (2021) also sees three areas of strategic importance for the IS space resulting from these emerging technologies. The first, according to them, is Engineering Trust and focuses on ensuring that technologies or software used are not error-prone so that the recommended actions resulting from IS systems are as accurate as possible to derive the maximum economic benefit from them. They list having sovereign cloud structures, non-fundable tokens, meaning authenticity certificates based on blockchain technology, or digitization of documents as examples. "Accelerating Growth" is another strategy that generative AI is designed to enable. On the one hand, to create added value through faster development, but also to generate technologies, drugs, or other insights through AI, which humans would never have thought of themselves (Gartner, 2021). According to Gartner (2021), "Sculpting Shape" will deploy composable application technologies that can adapt quickly to respond to innovations and gain a strategic advantage.

Gupta, Deokar, Iyer, Sharda, and Schrader (2018) argue that for IS purposes, a particular focus should be on technologies and innovations for data collection and decision makings, such as image and text recognition or AI. Furthermore, they see a trend, enabled by big data, to include sustainable aspects in non-technological decision-making systems, as these bring not only environmental and social benefits but also economic ones (Gupta et al., 2018; Schaltegger & Wagner, 2011).

However, these promising technologies must also be tested for feasibility and economic benefit by responsible parties, such as the chief information officer (CIO) or chief technical officer (CTO) before they can be transferred to business models.

3 Business Models

To be able to explain what new business models can arise from the emerging technologies, an explanation should first be given of what exactly business models are. There are several definitions of business models, which are similar but with a different focus. Generally speaking, business models describe "how the parts of a business fit together" (Magretta, 2002) or just “the logic of doing business” (Kurti, 2021). Teece (2007, p. 1329) states that business models “reflect management’s hypothesis about what customers want and how an enterprise can best meet those needs and get paid for doing so” and thus shows that it is also a question of external influences such as customer demands. Hedman and Kalling (2003) elaborate on this and name six external components that must be considered and influence each other (customers, competitors, offering, activities and organization, resources, and supply of factor and production inputs). They also consider another component important, which is to represent the constant change of conditions for the company. Kurti (2021) additionally highlights the importance of value propositions, customer value propositions, and market segments. These components also coincide with the Business Model Canvas by Osterwalder and Pigneuer (2010), one of the most famous frameworks for business models. The Business Model Canvas lets the creator answer questions about the domains mentioned to give the person further food for thought (Osterwalder, Pigneur, 2010).

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Details

Title
The development of Information Systems and emerging business models
College
Lund University  (School of Economics and Management)
Grade
2.7
Author
Year
2021
Pages
10
Catalog Number
V1160868
Language
English
Tags
Information Sytems, Business Models
Quote paper
Jan Harder (Author), 2021, The development of Information Systems and emerging business models, Munich, GRIN Verlag, https://www.grin.com/document/1160868

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