Understanding business analytics continuance in agile information system development projects: an expectation-confirmation perspective

Bawack, Ransome Epie; Ahmad, Muhammad Ovais

doi:10.1108/itp-10-2020-0681

Cited by 16 publications

(23 citation statements)

References 79 publications

(144 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(A) Speed and agility: The ability of BI&A to facilitate organizational agility under dynamic market conditions has been studied in the pre-pandemic literature [5]. Specifically, the value of BI&A to the sensing process needed to achieve agility is touted in past literature [26].…”

Section: Discussion: Bianda In the Post-pandemic Worldmentioning

confidence: 99%

Looking Ahead: Business Intelligence & Analytics Research in the Post-Pandemic New Normal

Marjanovic¹,

Ariyachandra²,

Dinter³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

The COVID 19 black swan event has disrupted every aspect of life in unprecedented ways, causing organizations to scramble to effectively sense and respond to the tumultuous business environment. Business intelligence and analytics (BI&A) capability has gained attention as a key weapon in the arsenal needed to combat turbulent times and to adjust to the post-pandemic new normal. Post-pandemic BI&A trends point to changes in organizational priorities for BI&A infrastructure that influence the traditional view of BI&A architecture and its role within an organization. As a result, new challenges and opportunities are emerging. This paper identifies and examines twelve key post-pandemic BI&A trends from industry practice and six major research themes. It also proposes an initial set of research questions that could inspire future research in BI&A in the post-pandemic new normal.

show abstract

Section: Discussion: Bianda In the Post-pandemic Worldmentioning

confidence: 99%

Looking Ahead: Business Intelligence & Analytics Research in the Post-Pandemic New Normal

Marjanovic¹,

Ariyachandra²,

Dinter³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…BA is a mixture of techniques, technologies and applications used to scrutinize a corporation's data and performance to transpire data-driven decision-making analytics for the corporation's future direction and investment plans (Bayrak, 2015;Kristoffersen et al, 2021). Data-driven corporations will manage their data as their corporate assets and actively look for ways to turn it into a competitive advantage against their competitors (Bawack and Ahmad, 2021). In this new era of big data, data-driven analytics is the way forward for major corporations in the manufacturing, information technology, marketing and logistics sectors.…”

Section: Business Analyticsmentioning

confidence: 99%

Predictive Analytics in Business Analytics: Decision Tree

2022

ADS

View full text Add to dashboard Cite

Purpose: Business Analytics was defined as one of the most important aspects of combinations of skills, technologies and practices which scrutinize a corporation's data and performance to transpire data-driven decision-making analytics for a corporation's future direction and investment plans. In this paper, much of the focus will be given to predictive analytics, which is a branch of business analytics that scrutinize the application of input data, statistical combinations and intelligence machine learning statistics on predicting the plausibility of a particular event happening, forecast future trends or outcomes utilizing on-hand data with the final objective of improving the performance of the corporation. While it has been around for decades, predictive analytics has gained much attention in the late 20th century. This technique includes data mining and big data analytics. Last but not least, the decision tree methodology, a supervised simple classification tool for predictive analytics, is fully scrutinized below for applying predictive business analytics and decision tree in business applications.Design/ Methodology/Approach: A systematic literature review was conducted in predictive analytics and decision tree. The literature review explains various fields' latest predictive analytics and decision trees. All the research papers are obtained from two databases: Web of Science and Scopus, which are widely acknowledged by the scientific and research communities that contain top-quality peer-reviewed journals.Findings: This study reviews the application of predictive analytics and decision tree in business decision-making across various fields.Practical implications: This paper will strongly contribute to providing significant inputs to analysts or researchers in business analytics, predictive analytics and decision tree as it presents recent evidence of the applications of various fields. This review will be in the interest of academics and practitioners in business analytics, especially predictive analytics.

show abstract

“…The majority of analytic projects fail for two main reasons: employees or users are lacking data skills and management lacking big data leadership and capabilities (Persaud, 2020). Recent studies have focused on BA tools rather than paying attention to users and the context in which they are used (Bawack & Ahmad, 2021;Conboy et al, 2020;Mikalef et al, 2019). Other studies focused on defining the concepts of BA, the effects of BA on the future of the businesses, and analysis of firms' performance and business values with the use of BA; however, there is a general lack of guidelines for companies to successfully adopt BA in their business environment (Abbasi et al, 2016;Acito & Khatri, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Other studies focused on defining the concepts of BA, the effects of BA on the future of the businesses, and analysis of firms' performance and business values with the use of BA; however, there is a general lack of guidelines for companies to successfully adopt BA in their business environment (Abbasi et al, 2016;Acito & Khatri, 2014). However, only a few empirical studies have been conducted to investigate the drivers and barriers of BA-related technology adoption in an organization (Chen et al, 2012;Chiang et al, 2018), which makes it difficult to generalize the results; confirming the fact that guidelines for BA adoption are still in the early stages (Abbasi et al, 2016;Bawack & Ahmad, 2021).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Empirical Investigation on Business Analytics in Software and Systems Development Projects

Ahmad

Rana

et al. 2022

Inf Syst Front

Self Cite

View full text Add to dashboard Cite

To create competitive advantages, companies are leaning towards business analytics (BA) to make data-driven decisions. Nevertheless, users acceptance and effective usage of BA is a key element for its success. Around the globe, organizations are increasingly adopting BA, however, a paucity of research on examining the drivers of BA adoption and its continuance is noticeable in the literature. This is evident in developing countries where a higher number of systems and software development projects are outsourced. This is the first study to examine BA continuance in the context of software and systems development projects from the perspective of Pakistani software professionals. The data was collected from 186 Pakistani software professionals working in software and systems development projects. The data were analyzed using partial least squares - structural equation modelling techniques. Our structural model explains 45% variance on BA continuance intention, 69% variance on technological compatibility, and 59% variance on perceived usefulness. Our results show that confirmation has a direct impact on BA continuance intention in software and systems projects. The study has both theoretical and practical implications for professionals in the field of business analytics.

show abstract

Understanding business analytics continuance in agile information system development projects: an expectation-confirmation perspective

Cited by 16 publications

References 79 publications

Looking Ahead: Business Intelligence & Analytics Research in the Post-Pandemic New Normal

Looking Ahead: Business Intelligence & Analytics Research in the Post-Pandemic New Normal

Predictive Analytics in Business Analytics: Decision Tree

An Empirical Investigation on Business Analytics in Software and Systems Development Projects

Contact Info

Product

Resources

About