Conceptualizing Task-Technology Fit for Technology-Pervaded Value Co-Creation

The Service-Dominant Logic of Marketing (S-D logic) provides a theoretical lens that enables service scholars to understand emerging phenomena related to servitization. The current service literature lacks contributions that holistically capture and explain technology-pervaded value co-creation processes. While studies in Information Systems research often focus on peculiarities of digital technology, the Service Marketing literature concentrates on the value co-creation process. Service researchers, however, call for integrative contributions that illuminate both technology and co-creation processes to enhance our understanding of technology-pervaded value co-creation. In response to this call, we conceptualize the Task-Technology Fit model according to the notion of S-D logic. Our conceptual model allows to integrate both digital technology’s specific characteristics and depicts their impact on cocreation performance and the perceived value-in-use. According to our model, resource integration involves conducting individual tasks that result in a task-technology fit, which in turn impacts co-creation performance and individual performances. All subsequent performances either co-create or codestruct the perceived value-in-use. We contribute to service research by deconstructing the relationship between pervasive digital technology and value co-creation processes. The proposed conceptual model provides a first step towards closing the gap between the siloed research streams in the IS and Service Marketing literature

Designing Predictive Maintenance for Agricultural Machines

The Digital Transformation alters business models in all fields of application, but not all industries transform at the same speed. While recent innovations in smart products, big data, and machine learning have profoundly transformed business models in the high-tech sector, less digitalized industries—like agriculture—have only begun to capitalize on these technologies. Inspired by predictive maintenance strategies for industrial equipment, the purpose of this paper is to design, implement, and evaluate a predictive maintenance method for agricultural machines that predicts future defects of a machine’s components, based on a data-driven analysis of service records. An evaluation with 3,407 real-world service records proves that the method predicts damaged parts with a mean accuracy of 86.34%. The artifact is an exaptation of previous design knowledge from high-tech industries to agriculture—a sector in which machines move through rough territory and adverse weather conditions, are utilized extensively for short periods, and do not provide sensor data to service providers. Deployed on a platform, the prediction method enables co-creating a predictive maintenance service that helps farmers to avoid resources shortages during harvest seasons, while service providers can plan and conduct maintenance service preemptively and with increased efficiency

Digitalization of Work Systems—An Organizational Routines’ Perspective

Digitalization is a hypernym that denotes the ground-shifting impact IT artifacts have on organizations. The term implicitly refers to core topics in Information Systems research, which now enfolds at increasing magnitude, speed, and reach. However, digitalization often lacks explicit references to domestic theories, concepts, and constructs in the Information Systems literature. Fundamental mechanisms that constitute digitalization as an interplay of organizations and information systems remain unexplored. The purpose of this paper is twofold. First, based on extending extant theory on organizational routines, we propose four patterns that conceptualize digitalization mechanisms as an interplay of organizational routines and IT artifacts. Second, we demonstrate how more complex transformation trajectories of routines unfold, by concatenating our patterns to form transformation stories. On either level of abstraction, further research can build on the proposed patterns to theorize on how the interplay of IT artifacts and organizational routines constitutes the digitalization of work systems

Designing Multi-sided Community Platforms for Local High Street Retail

The continuing rise of online retail comes at the expense of small and medium-sized stores in local high streets. Many cities now experience substantial vacancies and the decline of independent and family-owned stores, impeding citizens’ perceived quality of living. In this paper, we design the community platform ‘smartmarket²’, with which networks of local retailers interact with networks of customers to co-create a physical and at the same time digital customer experience in a high street. The platform connects with retailers’ information systems, while interfacing with in-store technologies to connect with potential customers’ smartphones. From a theoretical perspective, the platform exemplifies how previous research on value co-creation, multi-sided (engagement) platforms, and retail communities can complement each other to constitute online-offline customer experience. Based on comparing smartmarket² with rival IT artifacts, we abstract nascent design knowledge by conceptualizing a design theory for ‘community platforms for high street retail’ as a new class of IT artifacts. We conclude the paper with identifying how digital services—including cross-promotions, geographical recommender systems, and geospatial analytics—may be offered on the platform to leverage the competitive position of small and medium-sized retailers in local high streets through online-offline customer experience

A process mining maturity model: Enabling organizations to assess and improve their process mining activities

Organizations employ process mining to discover, check, or enhance process models based on data from information systems to improve business processes. Even though process mining is increasingly relevant in academia and organizations, achieving process mining excellence and generating business value through its application is elusive. Maturity models can help to manage interdisciplinary teams in their efforts to plan, implement, and manage process mining in organizations. However, while numerous maturity models on business process management (BPM) are available, recent calls for process mining maturity models indicate a gap in the current knowledge base. We systematically design and develop a comprehensive process mining maturity model that consists of five factors comprising 23 elements, which organizations need to develop to apply process mining sustainably and successfully. We contribute to the knowledge base by the exaptation of existing BPM maturity models, and validate our model through its application to a real-world scenario

Prevalence of human herpesviruses in biliary fluid and their association with biliary complications after liver transplantation

Background: Beta-herpesviruses are common opportunistic pathogens that cause morbidity after liver transplantation (LT). Methods: Objective of the study was to evaluate the prevalence and correlation of herpesviruses in bile, blood and liver tissue and to investigate their association with biliary complications and retransplantation (re-LT) free survival after LT. The study design is a single-center case-control study. We performed quantative polymerase chain reaction (qPCR) for herpesvirus 1–8 DNA in bile, blood and liver tissue of 73 patients after first LT and analyzed their clinical courses retrospectively. Results: The median follow-up was 48 months (range 2–102), during which a total of 16 patients underwent re-LT and 11 patients died. Of the patients, 46.5% received valganciclovir prophylaxis at the time of bile sample acquisition. Cytomegalovirus (CMV) (18.3%), human herpesvirus 6 (HHV-6) (34.2%), human herpesvirus 7 (HHV-7) (20.5%) and Epstein-Barr virus (EBV) (16.4%) were highly prevalent in bile after LT, while herpes simpex virus 1 and 2 (HSV-1, HSV-2), varicella-zoster virus (VZV) and human herpesvirus 8 (HHV-8) were not or rarely detected in bile. Valganciclovir prophylaxis did not reduce the prevalence of HHV-6 and HHV-7 in bile, but it did reduce the presence of CMV and EBV. The presence of HHV-6 in bile was associated with non-anastomotic biliary strictures (NAS) and acute cellular rejection (ACR). Conclusions: CMV, EBV, HHV-6 and HHV-7 are more prevalent in biliary fluid than in liver biopsy or blood serum after LT. HHV-6 and HHV-7 might be associated with biliary complications after LT. Biliary fluids might be an attractive target for routine herpesvirus detection

Towards a Reliable & Transparent Approach to Data-Driven Brand Valuation

Now accounting for more than 80% of a firm\u27s worth, brands have become essential assets for modern organizations. However, methods and techniques for the monetary valuation of brands are still under-researched. Hence, the objective of this study is to evaluate the utility of explanatory statistical models and machine learning approaches for explaining and predicting brand value. Drawing upon the case of the most valuable English football brands during the 2016/17 to 2020/21 seasons, we demonstrate how to operationalize Aaker\u27s (1991) theoretical brand equity framework to collect meaningful qualitative and quantitative feature sets. Our explanatory models can explain up to 77% of the variation in brand valuations across all clubs and seasons, while our predictive approach can predict out-of-sample observations with a mean absolute percentage error (MAPE) of 14%. Future research can build upon our results to develop domain-specific brand valuation methods while enabling managers to make better-informed investment decisions

The Impact of Process Automation on Manufacturers™ Long-Term Knowledge

The growing pressure on organizations to increase efficiency and quality intensifies process automation. Manufacturers have to manage knowledge consciously to understand processes and to transfer knowledge into automated machinery. Knowledge management i

Data-driven Customer Journey Mapping in Local High Streets: A Domain-specific Modeling Language

In high street retail, abundant research has focused on conceptualizing, empirically investigating, and improving customer experience at touchpoints throughout the customer journey. However, IT”including, smartphones, near-field communication, and broadb

A Method for Predicting Workarounds in Business Processes

Workarounds are performed intentionally by employees to bypass obstacles constraining their day-to-day work. These obstacles manifest from latent misfits in the interplay of information systems, organizational structure, and human agency. While workarounds are often mandatory for employees to perform their work, they can yield positive and negative effects on an organization’s performance. Process managers are supposed to identify workarounds early, promoting their positive while reducing their negative consequences. While related research has touched upon detecting workarounds in event logs that include data on completed processes, little is known on how to predict workarounds in a running business process. We set out to design a workaround prediction method using a deep learning approach. The IT artifact enables process managers to proactively intervene if workarounds are about to emerge in a business process, reducing their adverse effects while supporting organizational learning and process innovation