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ItemAction-Structure Paradox in a Strategic Information System Change Process( 2018-01-03)Any strategic Information System (IS) change process is at risk of a failure because of its inability to evolve as rapidly as the business environment. In this Grounded Theory study, aspects of socio-cognitive inertia arose in a 15-year customer-vendor relationship involving excessive optimism and trust in decision-making about technological options, knowledge sharing, and development practices. The pre-existing collaboration model was ultimately not supportive of the targeted strategic IS change. As a result, pressures to change the mode of operating emerged at the critical phase of initial rollout. This paper contributes to the IS change literature by presenting and theorizing an action-structure paradox identified during this study of strategic IS change.
ItemInformation Processing and Demand Response Systems Effectiveness: A Conceptual Study( 2018-01-03)This paper studies the effectiveness of demand response (DR) programs based on information processing theory. Following information processing theory, we propose a theoretical model which examines the fit between information processing needs and information processing capacity in an energy informatics framework. We analyze nature of tasks in DR programs and classify them into generic tasks categories based on the complexity of tasks. Our model further analyzes information processing capacity of DR within an automatic metering infrastructure (AMI) system and identifies four constituents of information processing capacity. Further, we extend task-technology fit and information processing theory to posit six propositions that explore the fit between elements of information processing capacity and needs and how the fit will impact DR outcomes. Our model contributes to the information system research connecting information and utility sector to administer the effectiveness of demand response systems that ultimately enhances environment sustainability.
ItemExtracting Causal Claims from Information Systems Papers with Natural Language Processing for Theory Ontology Learning( 2018-01-03)The number of scientific papers published each year is growing exponentially. How can computational tools support scientists to better understand and process this data? This paper presents a software-prototype that automatically extracts causes, effects, signs, moderators, mediators, conditions, and interaction signs from propositions and hypotheses of full-text scientific papers. This prototype uses natural language processing methods and a set of linguistic rules for causal information extraction. The prototype is evaluated on a manually annotated corpus of 270 Information Systems papers containing 723 hypotheses and propositions from the AIS basket of eight. F1-results for the detection and extraction of different causal variables range between 0.71 and 0.90. The presented automatic causal theory extraction allows for the analysis of scientific papers based on a theory ontology and therefore contributes to the creation and comparison of inter-nomological networks.