25+ Years of Business Intelligence and Analytics Minitrack at HICSS:  A Text Mining Analysis

Marjanovic, Olivera; Dinter, Barbara

doi:10.24251/hicss.2017.647

Cited by 10 publications

(10 citation statements)

References 18 publications

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“…The inclusion of senior executive interviews is intended to corroborate the findings from the job postings data (demand side) and the higher education data (supply side). Further, unlike traditional manual content analysis where the researcher develops the themes, text mining generates themes through a computer algorithm and the researchers then provide further explication of the themes based on domain knowledge (Marjanovic and Dinter, 2017). This way, researcher bias is avoided (Cretchley et al , 2010).…”

Section: Introductionmentioning

confidence: 99%

Key competencies for big data analytics professions: a multimethod study

Persaud

2020

ITP

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PurposeThis study aims to identify the precise competencies that employers are seeking for big data analytics professions and whether higher education big data programs enable students to acquire the competencies.Design/methodology/approachThis study utilizes a multimethod approach involving three data sources: online job postings, executive interviews and big data programs at universities and colleges. Text mining analysis guided by a holistic competency theoretical framework was used to derive insights into the required competencies.FindingsWe found that employers are seeking workers with strong functional and cognitive competencies in data analytics, computing and business combined with a range of social competencies and specific personality traits. The exact combination of competencies required varies with job levels and tasks. Executives clearly indicate that workers rarely possess the competencies and they have to provide additional training.Research limitations/implicationsA limitation is our inability to capture workers' perspectives to determine the extent to which they think they have the necessary competencies.Practical implicationsThe findings can be used by higher educational institutions to design programs to better meet market demand. Job seekers can use it to focus on the types of competencies they need to advance their careers. Policymakers can use it to focus policies and investments to alleviate skills shortages. Industry and universities can use it to strengthen their collaborations.Social implicationsMuch closer collaborations among public institutions, educational institutions, industry, and community organizations are needed to ensure training programs evolve with the evolving need for skills driven by dynamic technological changes.Originality/valueThis is the first study on this topic to adopt a multimethod approach incorporating the perspectives of the key stakeholders in the supply and demand of skilled workers. It is the first to employ text mining analysis guided by a holistic competency framework to derive unique insights.

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Section: Introductionmentioning

confidence: 99%

Key competencies for big data analytics professions: a multimethod study

Persaud

2020

ITP

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“…Nonetheless, the general idea -specifically considering a topical part of HICSS -is comparable to ours. Marjanovic and Dinter [13] presented a text-mining-based analysis of a minitrack (Business Intelligence) having been running for over 25 years. Dittes et al [14] gave an overview of eleven years of the Knowledge Management track; interestingly, it appeared in a journal and not in the HICSS Proceedings.…”

Section: A Brief Account Of the Historymentioning

confidence: 99%

A Bibliometric Analysis of the HICSS Software Technology Track

Grønli¹,

Majchrzak²,

Kaindl³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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The HICSS Software Technology track has a long tradition and many papers have been published as part of its history. Its impact in terms of citations, paper contributions, author share and community impact does not yet seem to have been investigated, though. In particular, software technology has evolved, and as it stands, it is of primary importance for mobile computing, the Internet-of-Things and Cyber-physical Systems. Hence, the development of these topics and the related impact of this track are of particular interest. In this paper, we present a bibliographic analysis as a first step towards such an investigation. We found that the history of the track is indeed noteworthy. Our results include finding a few extremely much cited papers, some curious tendencies, and a generally favourable outlook for HICSS. We found that indeed many papers in the history of this HICSS track addressed mobile technology and are highly cited. Together with providing insight into track, paper and author impact, we also raise questions worth of further investigation to understand the role of the HICSS Software Technology track and its impact on the academic field and society.

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“…BI systems' goal is to enable organizations to apply data-based decision-making efficiently [1]. Since BI systems' functional scope varies substantially, manifold definitions have been used to describe it [9]. The BI term has been used for simple reporting tools for technical infrastructures, like data warehouse systems, but also for advanced analytical tools, like data mining or predictive analytics [9].…”

Section: Theoretical Background 21 Ssbi Systemsmentioning

confidence: 99%

“…Since BI systems' functional scope varies substantially, manifold definitions have been used to describe it [9]. The BI term has been used for simple reporting tools for technical infrastructures, like data warehouse systems, but also for advanced analytical tools, like data mining or predictive analytics [9]. Chen et al, who depict BI (and analytics) as encompassing underlying data processes and analytical technologies, as well as "business-centric practices and methodologies," propose a holistic definition to grasp this diversity conceptually [10, p. 1166].…”

Section: Theoretical Background 21 Ssbi Systemsmentioning

confidence: 99%

Understanding User Uncertainty during the Implementation of Self-Service Business Intelligence: A Thematic Analysis

Weiler¹,

Matt²,

Heß³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Owing to Self-Service Business Intelligence (SSBI) systems' transformative power for organizations, substantial user uncertainties often blight their potential. Although these uncertainties pose a significant threat to effective SSBI implementation, their sources and determinants remain unclear. We conducted semi-structured interviews with 15 current users of a recently implemented SSBI system to empirically explore the relevant factors of user uncertainty. We undertook a rigorous thematic analysis of the collected data, thereafter developing a thematic map to visualize user uncertainties. This map uncovered three unexplored important factors (work routine change, social dynamics and fear of AI) for future research. Our findings show that users are not only perturbed by "hard" factors (e.g. a lack of technical understanding), but also by "soft" factors (social dynamics, fear of AI and nontransparency). Practitioners can use the thematic map to identify and observe potential uncertainties and to develop adequate procedures.

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25+ Years of Business Intelligence and Analytics Minitrack at HICSS: A Text Mining Analysis

Abstract: Abstract

Cited by 10 publications

References 18 publications

Key competencies for big data analytics professions: a multimethod study

Key competencies for big data analytics professions: a multimethod study

A Bibliometric Analysis of the HICSS Software Technology Track

Understanding User Uncertainty during the Implementation of Self-Service Business Intelligence: A Thematic Analysis

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