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The question of why individuals adopt information technology has been present in the information systems research since the past quarter century. One of the most used models for predicting the technology usage was introduced by Fred David: The Technology Acceptance Model (TAM). It describes the influence of perceived usefulness and perceived ease of use on attitude, behavioral intention and system usage. The first two mentioned factors in turn are influenced by external variables. Although a plethora of papers exists about the TAM , an extensive analysis of the role of the external variables in the model is still missing. This paper aims to give an overview ove the most important variables. In an extensive literature review, we identified 763 relevant papers, found 552 unique single extenal variables, characterized the most important of them, and described the frequency of their appearance. Additionally, we grouped these variables into four categories (organizational characteristis, system characteristics, user personal characteristics, and other variables). Afterwards we discuss the results and show implications for theory and practice.
The stimulation of user engagement has received significant attention in extant research. However, the theory of antecedents for user engagement with an initial electronic word-of-mouth (eWoM) communication is relatively less developed. In an investigation of 576 unique user postings across independent Facebook (FB) communities for two German firms, we contribute to the extant knowledge on user engagement in two different ways. First, we explicate senders’ prior usage experience and the extent of their acquaintance with other community members as the two key drivers of user engagement across a product and a service community. Second, we reveal that these main effects differ according to the type of community. In service communities, experience has a stronger impact on user engagement; whereas, in product communities, acquaintance is more important.
This paper examines the efficacy of social media systems in customer complaint handling. The emergence of social media, as a useful complement and (possibly) a viable alternative to the traditional channels of service delivery, motivates this research. The theoretical framework, developed from literature on social media and complaint handling, is tested against data collected from two different channels (hotline and social media) of a German telecommunication services provider, in order to gain insights into channel efficacy in complaint handling. We contribute to the understanding of firm’s technology usage for complaint handling in two ways:
(a) by conceptualizing and evaluating complaint handling quality across traditional and social media channels and (b) by comparing the impact of complaint handling quality on key performance outcomes such as customer loyalty, positive word-of-mouth, and crosspurchase intentions across traditional and social media channels.
This research addresses the question of why employees use enterprise social networks (ESN). Against the background of technology acceptance research, we propose an extended unified theory of acceptance and use of technology (UTAUT) model, adapt it to an ESN context, and test our model against data from ESN users of large and medium-sized enterprises. We use partial least squares structural equation modeling to gain insights into the determinants of ESN use. This paper contributes to ESN acceptance research by evaluating a model containing determinants of ESN use. It also examines the effects of determinants on five different usage dimensions of ESN. The results reveal that facilitating conditions are the main driver of ESN use while the impact of intention to use is comparably small. Implications for theory and practice are discussed.
Smart cities are considered data factories that generate an enormous amount of data from various sources. In fact data is the backbone of any smart services. Therefore, the strategic beneficial handling of this digital capital is crucial for cities. Some smart city pioneers have already written down their approach to data in the form of data strategies, but what should a city's data strategy include, and how can the goals and measures defined in the strategies be operationalized? This paper addresses these questions by looking closely at the data strategies of cities in Germany and the top three countries in the EU Digital Economy and Society Index. The in-depth analysis of 8 city data strategies has yielded 11 dimensions that cities should consider in their data strategy. These are relevance of data, principles, methods, data sharing, technology, data culture, data ethics, organizational structure, data security and privacy, collaborations, data literacy. In addition, data governance is a concept to put these 11 strategic dimensions into practice through standardization measures, training programs, and defining roles and responsibilities by developing a data catalog.
The benefits of urban data cannot be realized without a political and strategic view of data use. A core concept within this view is data governance, which aligns strategy in data-relevant structures and entities with data processes, actors, architectures, and overall data management. Data governance is not a new concept and has long been addressed by scientists and practitioners from an enterprise perspective. In the urban context, however, data governance has only recently attracted increased attention, despite the unprecedented relevance of data in the advent of smart cities. Urban data governance can create semantic compatibility between heterogeneous technologies and data silos and connect stakeholders by standardizing data models, processes, and policies. This research provides a foundation for developing a reference model for urban data governance, identifies challenges in dealing with data in cities, and defines factors for the successful implementation of urban data governance. To obtain the best possible insights, the study carries out qualitative research following the design science research paradigm, conducting semi-structured expert interviews with 27 municipalities from Austria, Germany, Denmark, Finland, Sweden, and the Netherlands. The subsequent data analysis based on cognitive maps provides valuable insights into urban data governance. The interview transcripts were transferred and synthesized into comprehensive urban data governance maps to analyze entities and complex relationships with respect to the current state, challenges, and success factors of urban data governance. The findings show that each municipal department defines data governance separately, with no uniform approach. Given cultural factors, siloed data architectures have emerged in cities, leading to interoperability and integrability issues. A city-wide data governance entity in a cross-cutting function can be instrumental in breaking down silos in cities and creating a unified view of the city’s data landscape. The further identified concepts and their mutual interaction offer a powerful tool for developing a reference model for urban data governance and for the strategic orientation of cities on their way to data-driven organizations.
Autonomous driving is becoming the next big digital disruption in the automotive industry. However, the possibility of integrating autonomous driving vehicles into current transportation systems not only involves technological issues but also requires the acceptance and adoption of users. Therefore, this paper develops a conceptual model for user acceptance of autonomous driving vehicles. The corresponding model is tested through a standardized survey of 470 respondents in Germany. Finally, the findings are discussed in relation to the current developments in the automotive industry, and recommendations for further research are given.
Many start-ups are in search of cooperation partners to develop their innovative business models. In response, incumbent firms are introducing increasingly more cooperation systems to engage with start-ups. However, many of these cooperations end in failure. Although qualitative studies on cooperation models have tried to improve the effectiveness of incumbent start-up strategies, only a few have empirically examined start-up cooperation behavior. Considering the lack of adequate measurement models in current research, this paper focuses on developing a multi-item scale on cooperation behavior of start-ups, drawing from a series of qualitative and quantitative studies. The resultant scale contributes to recent research on start-up cooperation and provides a framework to add an empirical perspective to current research.
Theoretical foundation, effectiveness, and design artefact for machine learning service repositories
(2022)
Machine learning (ML) has played an important role in research in recent years. For companies that want to use ML, finding the algorithms and models that fit for their business is tedious. A review of the available literature on this problem indicates only a few research papers. Given this gap, the aim of this paper is to design an effective and easy-to-use ML service repository. The corresponding research is based on a multi-vocal literature analysis combined with design science research, addressing three research questions: (1) How is current white and gray literature on ML services structured with respect to repositories? (2) Which features are relevant for an effective ML service repository? (3) How is a prototype for an effective ML service repository conceptualized? Findings are relevant for the explanation of user acceptance of ML repositories. This is essential for corporate practice in order to create and use ML repositories effectively.
The advent of chatbots in customer service solutions received increasing attention by research and practice throughout the last years. However, the relevant dimensions and features for service quality and service performance for chatbots remain quite unclear. Therefore, this research develops and tests a conceptual model for customer service quality and customer service performance in the context of chatbots. Additionally, the impact of the developed service dimensions on different customer relationship metrics is measured across different service channels (hotline versus chatbots). Findings of six independent studies indicate a strong main effect of the conceptualized service dimensions on customer satisfaction, service costs, intention to service reusage, word-of-mouth, and customer loyalty. However, different service dimensions are relevant for chatbots compared to a traditional service hotline.