Assessing Website Performance In The Line Of The Is Impact Model

This research proposal presents a novel study that aims to contribute to the understanding of factors that impact on the performance of Australian university business school websites. The proposed study addresses the current limitations of website performance measurement and incorporates a multilevel perspective that accounts for user and organisational perspectives on website performance. This study adopts the IS-Impact model, developed by G able et al. (2008) as its primary theoretical foundation and applies Shannon & Weaver’s (1949) Communication Theory to develop a conceptual model of website performance as a tool for understanding multilevel website usage as a measure of website performance. The study employs a two-phase quantitative survey research method incorporating an exploratory and confirmatory phase. The exploratory phase aims to test the completeness and the applicability of the IS-Impact model’s dimensions and measures in the university website context. The confirmatory phase aims to further validate the model and instrument derived from the exploratory phase, as well as to reconfirm the model and measures using quantitative data

Decision-Making Performance in Big Data Era: The Role of Actual Business Intelligence Systems Use and Affecting External Constraints

Business Intelligence (BI) has received wide recognition in the business world as a tool to ad-dress ‘big’ data-related problems, to help managers understand their businesses and to assist them in making effective decisions. To date, however, there have been few studies which have clearly articulated a theoretically grounded model that explains how the use of BI systems provides benefits to organisations, or explains what factors influence the actual use of BI systems. To fully achieve greater decision-making performance and effective use of BI, we contend that BI systems integration with a systems user’s work routine (dependence on the systems) is essential. Following this argument, we examine the effects of system dependent use along with effective use (infusion) on individual’s decision-making performance with BI. Additionally, we pro-pose that a fact-based decision-making culture, and data quality of source systems are constraints factors that impact on BI system dependence and infusion. We adopt a quantitative method approach. Specifically, we will conduct a two-wave cross-sectional survey targeting 400 North American BI users who describe themselves as both using a BI system and making decision using data from the system. We expect to make an important theoretical contribution to BI literature by providing a model that explains the dimensions of actual BI system use, and makes a practical contribution by providing insights into how organisational external constraints facilitate BI dependence and infusion in the pursuit of BI-enabled performance gain

AI and the Future of Business Strategists: A Review and Research Agenda

Artificial Intelligence has received increased attention from multiple research disciplines, including strategic management and information systems. Despite such heightened interest, there is a noticeable absence of a comprehensive framework to explain how business strategists work with AI to develop business strategies. This paper develops such a framework to illustrate the process of business strategists working with AI to develop business strategies. We also conducted a systematic literature review of AI in business strategy research and used the developed framework to structure the analysis. From the findings, we reveal which parts of the framework have been studied and which are still in need of further research. In doing so, this study makes important contributions by (1) proposing a comprehensive framework of strategy workers and AI delegation process, (2) identifying the currently reported contributions of AI and business strategy research, and (3) identifying promising venues and critical research questions for future research

Extending the theory of effective use: The impact of enterprise architecture maturity stages on the effective use of business intelligence systems

Business Intelligence (BI) has received wide recognition in IT, business and academia. Through the use of BI businesses are able to address 'big data' related problems for better management decisions across all industries. However, few studies have clearly articulated a theoretically grounded model or provided empirical data to explain what factors influence the effective use of BI. Drawing on the theory of effective use (TEU) and literature on enterprise architecture, business intelligence and IT user performance, I developed a research model to examine the impact of different stages of enterprise architecture maturity on the representational fidelity of BI, which has been identified as one of the critical dimensions of effective use of BI influencing managers' decisionmaking performance. The study will adopt a mixed methods approach combining qualitative and quantitative data collection from managers in BI-based organizations. This study makes an important theoretical contribution to the study of effective use of BI, and also makes a practical contribution by providing insights into the creation of environments to facilitate more effective BI use in the pursuit of better decision-making performance

Getting value from Business Intelligence systems: a review and research agenda

Much of the research on Business Intelligence (BI) has examined the ability of BI systems to help organizations address challenges and opportunities. However, the literature is fragmented and lacks an overarching framework to integrate findings and systematically guide research. Moreover, researchers and practitioners continue to question the value of BI systems. This study reviews and synthesizes empirical Information System (IS) studies to learn what we know, how well we know, and what we need to know about the processes of organizations obtaining business value from BI systems. The study aims to identify which parts of the BI business value process have been studied and are still most in need of research, and to propose specific research questions for the future. The findings show that organizations appear to obtain value from BI systems according to the process suggested by Soh and Markus (1995), as a chain of necessary conditions from BI investments to BI assets to BI impacts to organizational performance; however, researchers have not sufficiently studied the probabilistic processes that link the necessary conditions together. Moreover, the research has not sufficiently covered all relevant levels of analysis, nor examined how the levels link up. Overall, the paper identified many opportunities for researchers to provide a more complete picture of how organizations can and do obtain value from BI