The aim of this paper is the scientific development of a maturity model concerning the digital transformation of companies within the manufacturing industry’s supply chain. The rather “broad” and dispersed “mega-trend” of digitalization is expected to play an increasingly important role for companies as well as for the (digital) supply chain of the future. Such a model comprises the objective of addressing fundamental components, complementary innovations and relevant terminologies, like smart products, Cyber-Physical Systems (CPS) and Big Data Analytics.
Scientific rigor is achieved through conducting grounded theory research and in-depth interviews as methods of data collection and evaluation.
Furthermore, relevant aspects concerning the development and construction of maturity models are discussed, before a suitable and scientifically elaborated maturity model concerning digitalization emerges from the course of investigation and its value for economic practice as well as for the scientific community is specified.
In the promotion of sustainable consumer behaviour, it is important to establish a mental relation be- tween one’s behaviour and its environmental impact. High hopes rest on timely feedback on personal energy consumption in order to create this link. Great efforts are being put into the development of information systems to achieve this, and smart meters are being deployed as an enabling technology worldwide. Recent smart metering trials, which provide feedback on aggregate household electricity consumption, report moderate savings of 2-5%. There is, however, a vivid controversy about consumer interest and continuous use of these technologies in the long run. This uncertainty introduces substantial risk to the deployment of these technologies, as the persistence of savings is crucial for the cost-benefit analyses and scalability of these programs. This paper investigates the long-term stability of the behav- iour change induced by a real-time feedback technology. Our initial study found average energy savings of 22% for the target behaviour. In this study, we analyse 17,612 data points collected in a one-year follow-up field study. The results suggest that the effects of behaviour-specific feedback on energy con- sumption do not exhibit a significant decay, indicating that this kind of technology successfully induces persistent behaviour change.
Self-tracking, or the desire to quantify one’s own behaviour by means of personal information systems, has developed from a niche activity for early adopters to a mass phenomenon. Despite its increasing spread in society, however, little is known about the drivers of technology adoption and use in this specific domain. Addressing this gap, the research-in-progress at hand aims to (1) theorize on the role of self-tracking attitudes – defined as specific consumer attitudes in the self-tracking context, and (2) present a research plan with the goal of developing a self-report scale capable to measure selftracking attitudes. We present the results of a first literature review and of 24 explorative expert interviews conducted to identify relevant cognitive, affective and conative concepts related to self-tracking attitudes. The results constitute a first step to develop a measurement scale that aims to contribute to adoption research and the development of successful self-tracking systems.
Various models in Information Systems (IS) research seek to understand why individuals embrace or resist the adoption or use of a technology. Different models analyze the factors shaping user intentions at different stages of technology adoption and use. Yet, less is known how the factors shaping adoption intention sub- sequently evolve into continuous usage intention as users become (more) familiar with the technology. This paper investigates participants’ (N=549) adoption and continuous usage intention of a smartphone appli- cation for energy efficiency twice: at two different stages of experience, but for the same technology, in the same setting, and in particular with the same sample. In both cases, we use the Unified Theory of Ac- ceptance and Use of Technology (UTAUT1&2). While UTAUT explains adoption intention well, we find only moderate support for continuous usage intention. In line with prior research, our data suggests that beliefs are updated from adoption to continuous usage stage.
We study the emergence and evolution of trust in larger societies. We focus on the thin notion of trust, that is the trust needed for interacting with hitherto unknown individuals encountered for just a single interaction. Our model builds upon well-established theoretical knowledge of the determinants of trust. These works identify parameters such as the existence of networks, the level of mobility or the percentage of trust-abusing agents in a society. While the influence of each of these factors individually is well-established by empirical work, a precise account of the interplay of these factors is lacking. To bridge this gap, we devise a multi agent computer simulation that allows a fine grained analysis of the dynamic processes governing the emergence of trust and its dependencies upon these parameters. We model agents using a bayesian learning framework about the value of trust, taking both individual and social information into account.