A META-ANALYTIC REVIEW OF SOCIAL MEDIA STUDIES

Social media such as social networking sites, blogs, micro-blogs, Wikis, are increasingly and widely used in our daily lives. In the information system (IS) discipline, social media have become a hot research area and draw the attention of many scholars. The paper systematically reviewed social media studies published in Association for Information Systems (AIS) listed top 20 journals from 2009 to 2013. The publication time, journal preferences, research objects and research topics are discussed. Generally, the current social media studies including four areas, namely user, management, technology and information. Each area has distinct focuses and topics. By thoroughly analyzing the research topics, the authors formulate our projections and recommendations for future social media studies

Systematic Review of Information System Journals on the Use of Web 2.0 in Mass Emergencies

The study reviewed 32 literature from MIS Quarterly and European Journal of Information Systems on the use of web 2.0 in humanitarian emergencies. The exercise adopts Wolfswinkel, Furtmueller & Wilderom (2013); Webster & Watson, (2002) approach to systematic review where methods, themes, authors and the use of tools were studied, analysed and synthesized. The insight gained from the study reveals the availability of interoperability framework in EU and USA. Others are paucity of authors from the developing world, gaps in the use of ICT tools in the humanitarian situations as well as the overwhelming use of quantitative approach across the two journals. Findings from the review will help in identifying gaps and crafting research question. Keywords: ICT, Humanitarian Emergencies, Systematic Review, MIS Quarterly, European Journal of Information System

The Role of Emotions in Cross-border Mergers & Acquisitions : A Systematic Review of the Inter-disciplinary Literature and Future Research Agenda

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Getting a Clean Shot on a Blurred Target: Improving Targeting for Strategic Scanning through Action Research in 10 French Organizations

Targeting comprises defining the part of the business environment that corresponds to organizations’ strategic objectives and priorities. Targeting is not an easy process because it includes the interaction of managers who come from different organizational units that might have a fragmentary and blurred understanding of the overall issue. Through an action research, we designed and evaluated a GSS to help managers target strategic scanning in fuzzy contexts. Evaluations through interventions in 10 French organizations allowed both participants to achieve relevant targets and researchers to propose four major improvements to targeting activities: 1) use suggested lists of actors and topics as starting points to trigger and facilitate discussions, 2) define actor and topic importance to produce useful targeting results, 3) evaluate the organization’s perceived capacity to be informed early enough, and 4) define a mechanism to signal scanning relevancy in the short, mid-, or long term. From a management perspective, our results help managers in their strategic scanning activity by 1) identifying information needs for strategically scanning fuzzy subjects, 2) reducing risk of strategic scanning failure, 3) enabling organizations to assess their scanning capabilities, 4) identifying scanning priorities according to a temporal horizon, and 5) fostering teamwork participation

A systematic mapping study

Corte-Real, N., Ruivo, P., & Oliveira, T. (2014). The diffusion stages of business intelligence & analytics (BI&A):: A systematic mapping study. In Procedia Technology (Vol. 16, pp. 172-179). (Procedia Technology). DOI: 10.1016/j.protcy.2014.10.080Business intelligence & analytics (BI&A) has evolved to become a foundational cornerstone of enterprise decision support. Since the way BI&A is implemented and assimilated is quite different among organizations is important to approach BI&A literature by four selected diffusion stages (adoption, implementation, use and impacts of use). The diffusion stages assume a crucial importance to track the BI&A evolution in organizations and justify the investment made. The main focus of this paper is to evidence BI&A research on its several diffusion stages. It provides an updated bibliography of BI&A articles published in the IS journal and conferences during the period of 2000 and 2013. A total of 30 articles from 11 journals and 8 conferences are reviewed. This study contributes to the BI&A research in three ways. This is the first systematic mapping study focused on BI&A diffusion stages. It contributes to see how BI&A stages have been analyzed (theories used, data collection methods, analysis methods and publication source). Finally, it observes that little attention has been given to BI&A post-adoption stages and proposes future research line on this area.publishersversionpublishe

Improving Strategic Decision-Making through the Use of Business Analytics: A Resource-Based View

While business analytics is suggested to improve organizational decision-making, more empirical research is needed to substantiate this proposition. This study draws on the resource-based view to understand how an organization can use business analytics to improve its strategic decision making (SDM). The analysis of 218 survey responses from UK firms shows that the use of business analytics is related to rational SDM positively and intuitive SDM negatively, while environmental scanning mediates the relationship between the use of business analytics and rational SDM. The findings suggest that an organization can improve its SDM by enhancing its analytics and environmental scanning capabilities

BIG DATA ANALYTICS TOOLS: A BIBLIOMETRIC LITERATURE REVIEW

Analytics as a field is rapidly growing because of businesses need to tackle increasingly large and complex datasets to gain a competitive edge in the business environment. Currently there are two distinct types of analytics technologies: proprietary and open-source. Each has their distinct strengths and weaknesses, but which is relevant in today business environment? Questions like this are important for businesses wanting utilize these technologies in order to become larger and more efficient. This research answered this question and it was found that both proprietary and open-source technologies are equally relevant in current analytics research. Because of this, businesses should be more aware of the analytics field and how both types of technologies could benefit their current operations and should strategically utilize both types to meet their specific data needs

Of BI research : a tale of two communities

The Business intelligence (BI) literature is in flux, yet the knowledge about its varying theoretical roots remains elusive. This state of affairs draws from two different scientific communities (informatics and business) that have generated multiple research streams, which duplicate research, neglect each other’s contributions, and overlook important research gaps. In response, we structure the BI scientific landscape and map its evolution to offer scholars a clear view of where research on BI stands and the way forward. For this endeavor, we systematically review articles published in top-tier ABS journals and identify 120 articles covering 35 years of scientific research on BI. We then run a co-citation analysis of selected articles and their reference lists. This yields the structuring of BI scholarly community around six research clusters: Environmental Scanning (ES), Competitive Intelligence (CI), Market Intelligence (MI), Decision Support (DS), Analytics Technologies (AT), and Analytics Capabilities (AC). The Co-citation network exposed overlapping and divergent theoretical roots across the six clusters and permitted mapping the evolution of BI research following two pendulum swings. Our article contributes by 1) structuring the theoretical landscape of BI research, 2) deciphering the theoretical roots of BI literature, 3) mapping the evolution of BI scholarly community, and 4) suggesting an agenda for future research.© 2020 Emerald Publishing Limited. This manuscript version is made available under the Creative Commons Attribution–NonCommercial 4.0 International (CC BY–NC 4.0) license, https://creativecommons.org/licenses/by-nc/4.0/fi=vertaisarvioitu|en=peerReviewed

ARTIFACT EVALUATION IN INFORMATION SYSTEMS DESIGN-SCIENCE RESEARCH – A HOLISTIC VIEW

Design science in Information Systems (IS) research pertains to the creation of artifacts to solve reallife problems. Research on IS artifact evaluation remains at an early stage. In the design-science research literature, evaluation criteria are presented in a fragmented or incomplete manner. This paper addresses the following research questions: which criteria are proposed in the literature to evaluate IS artifacts? Which ones are actually used in published research? How can we structure these criteria? Finally, which evaluation methods emerge as generic means to assess IS artifacts? The artifact resulting from our research comprises three main components: a hierarchy of evaluation criteria for IS artifacts organized according to the dimensions of a system (goal, environment, structure, activity, and evolution), a model providing a high-level abstraction of evaluation methods, and finally, a set of generic evaluation methods which are instantiations of this model. These methods result from an inductive study of twenty-six recently published papers

Examining Competitive Intelligence Using External and Internal Data Sources: A Text Mining Approach

Competitive intelligence (CI) is the practice of studying competitors and competitive environment in support of firm’s strategic decision-making process. Currently, competitors are usually studied from business profile information and reports edited by CI professionals. While being inefficient and expensive in labor and resources, their results are often incomplete and lack objectivity. Some existing literatures introduced text mining to leverage Web information for CI usage. Despite improving on coverage, most of these analyses identify competitors using name co-occurrences from a single data source. The validity and reliability of these studies remain questionable. Our experiment demonstrates that syntactic level text mining can lead to improvements on CI performance. It also shows that the selection of different online data sources and competitor name extraction methods have different implications on CI outcome