The barriers to big data adoption in developing economies

Alalawneh, Ammar A. Faleh; Alkhatib, Saleh Fahed

doi:10.1002/isd2.12151

Cited by 20 publications

(10 citation statements)

References 67 publications

(103 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, technological challenges related to hardware, software, storage infrastructure and so on (Adrian et al. , 2018) and organisational challenges related to the high hierarchy of the organisational structure, the lack of top management support for big data analytics programs (Alalawneh and Al-Khatib, 2021; Sun et al. , 2018) and organisational policies and procedures about confidentiality and privacy may lead to organisations not adopting big data analytics programs (Sun et al.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Intellectual capital and innovation performance: the moderating role of big data analytics: evidence from the banking sector in Jordan

Al-Khatib¹

2022

EMJB

View full text Add to dashboard Cite

PurposeThe purpose of this study is to identify the impact of intellectual capital on the innovation performance of the Jordanian banking sector and identify the moderating role of big data analytics.Design/methodology/approachFor this study's purposes, 333 questionnaires were analysed. Convergent validity, discriminant validity and reliability tests were performed through structural equation modelling (SEM) in the Smart-PLS program. A bootstrapping technique was used to analyse the data.FindingsEmpirical results showed that each of the components of intellectual capital and big data analytics explains 63.5% of the variance in innovation performance and that all components of intellectual capital have a statistically significant impact on innovation performance. The results also revealed that the relationship between structural capital and innovation performance is moderated through big data analytics.Research limitations/implicationsThis cross-sectional study provides a snapshot at a given moment in time, a methodological limitation that affects the generalisation of its results, and the results are limited to one country.Practical implicationsThis study promotes the idea of focusing on components of intellectual capital to enhance innovation performance in the Jordanian banking sector and knowing the effect of big data analytics in this relationship.Social implicationsThis study makes recommendations for financial policymakers to improve the effectiveness of intellectual capital practices and innovation performance in the context of big data analytics.Originality/valueThis study has important implications for leaders in the Jordanian banking sector, in general, as the study highlights the importance of intellectual capital to enhance the innovation performance, especially in light of the big data analytics in this sector, and thus increase the innovative capabilities of this banks, which leads to an increase in the level of innovation.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…, 2018). Challenges that may limit the optimum use of big data analytics are related to the external environment, such as governmental and legal procedures and public technical infrastructure (Alalawneh and Al-Khatib, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Intellectual capital and innovation performance: the moderating role of big data analytics: evidence from the banking sector in Jordan

Al-Khatib¹

2022

EMJB

View full text Add to dashboard Cite

show abstract

“…The organizational structure has been extensively mentioned as one of the most important barriers that influence BDA projects [20]. Therefore, the adoption of BDA requires new organizational structures [16].…”

Section: Structurementioning

confidence: 99%

The O-Factor: Aligning Organizational Arrangements with Big Data Analytics Diffusion

Elsman¹,

Borgman²

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

This study aims to better understand how and why organizational arrangements of Big Data Analytics (BDA) evolve over time in established firms. As BDA initiatives grow in scope and importance, organizational arrangements tend to change, with changes impacting the success of the initiative. This study focuses on the importance of four constructs influencing organizational arrangements during BDA diffusion: the analytics structure, the leadership role, the culture, and the employee skills. Propositions derived from the literature guide the analysis of seven case studies of organizations adopting BDA. The findings help to understand BDA diffusion through (1) aligning structure with business value creation, (2) (new) leadership that trusts and shows exemplary usage of BDA, (3) a culture of trust with constant experimentation for business opportunities and (4) more diversified employee roles. A discussion of academic and managerial implications and suggestions for future research completes this study.

show abstract

“…However, hiring such experts involve a high cost that becomes an obstacle for organizations to invest in BDA (Alharthi et al , 2017). Therefore, due to a lack of financial readiness to cover the cost of BDA, investment in BDA may lead to failure (Alalawneh and Alkhatib, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

“…This category accounted for 8.385% of the variance. Alalawneh and Alkhatib (2020) argued that organizations should provide good training/ education programs, sufficient resources, supportive facilities and appropriate consulting services to facilitate investment in BDA.…”

Section: Findings Of Statistical Analysismentioning

confidence: 99%

Analysis of barriers intensity for investment in big data analytics for sustainable manufacturing operations in post-COVID-19 pandemic era

Kumar

Singh

2021

JEIM

View full text Add to dashboard Cite

PurposeThe study presents various barriers to adopt big data analytics (BDA) for sustainable manufacturing operations (SMOs) post-coronavirus disease (COVID-19) pandemics. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the graph theory matrix approach (GTMA) is applied.Design/methodology/approachThe study presents various barriers to adopt BDA for the SMOs post-COVID-19 pandemic. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the GTMA is applied.FindingsThe study identifies barriers to investment in BDA implementation. It categorizes the barriers based on factor analysis and computes the intensity for each category of a barrier for BDA investment for SMOs. It is observed that the organizational barriers have the highest intensity whereas the human barriers have the smallest intensity.Practical implicationsThis study may help organizations to take strategic decisions for investing in BDA applications for achieving one of the sustainable development goals. Organizations should prioritize their efforts first to counter the barriers under the category of organizational barriers followed by barriers in data management and human barriers.Originality/valueThe novelty of this paper is that barriers to BDA investment for SMOs in the context of Indian manufacturing organizations have been analyzed. The findings of the study will assist the professionals and practitioners in formulating policies based on the actual nature and intensity of the barriers.

show abstract

The barriers to big data adoption in developing economies

Cited by 20 publications

References 67 publications

Intellectual capital and innovation performance: the moderating role of big data analytics: evidence from the banking sector in Jordan

Intellectual capital and innovation performance: the moderating role of big data analytics: evidence from the banking sector in Jordan

The O-Factor: Aligning Organizational Arrangements with Big Data Analytics Diffusion

Analysis of barriers intensity for investment in big data analytics for sustainable manufacturing operations in post-COVID-19 pandemic era

Contact Info

Product

Resources

About