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Article
Publication date: 2 August 2023

Rukma Ramachandran, Vimal Babu and Vijaya Prabhagar Murugesan

This systematic literature review aims to explore the adoption, global acceptance and implementation of human resources (HR) analytics (HRA) by reviewing literature on the…

Abstract

Purpose

This systematic literature review aims to explore the adoption, global acceptance and implementation of human resources (HR) analytics (HRA) by reviewing literature on the subject. HRA adoption can assist HR professionals in managing complex procedures and making strategic human resource management (SHRM) decisions more effectively. The study also aims to identify the applications of analytics in various disciplines of management.

Design/methodology/approach

The review is conducted using a domain-based structured literature review (SLR), emphasizing the diffusion of innovative thinking and the adoption process of HRA among early adopters. The philosophical stances are analyzed with the combination of research onion model and PRISMA protocol. Secondary data are gathered from published journals, books, case studies, conference proceedings, web pages and media stories as the primary source of information.

Findings

The study finds that skilled professionals and management assistance can significantly impact adoption intentions, enabling professionals to deal with analytics. The examples and analytical models provided by early adopters allow managers to manage complex processes and make SHRM decisions.

Research limitations/implications

The study suggests that the lack of use of quantitative techniques is a key limitation and should be considered in future studies. Despite the rise in the number of research papers on HRA, its application in the workplace remains limited.

Practical implications

This research can assist managers in implementing HRA and help resolve complex and inefficient processes, making SHRM decisions.

Originality/value

This study adds to the existing body of knowledge on how HRA can aid a company's efficacy and performance and can be considered one of the first to link adoption and HRA.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 4 September 2023

P. Ravi Kiran, Akriti Chaubey and Rajesh Kumar Shastri

The research paper aims to analyse the scholarly literature on advancing HR analytics as an intervention for attrition, a problem that lingers on organisational performance. This…

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Abstract

Purpose

The research paper aims to analyse the scholarly literature on advancing HR analytics as an intervention for attrition, a problem that lingers on organisational performance. This study aspires to provide an in-depth literature review and critically assess the knowledge gaps in HR analytics and attritions within organisational performance.

Design/methodology/approach

The review analyses the corpus of 196 research articles published in ostensible journals between 2011 and 2023. To identify research gaps and provide valuable insights, this study synthesises relevant studies using School of thought (S), Context (C), Methodology (M), Triggers (T), Barriers (B), Facilitators (F) and Outcomes (O) (SCM-TBFO framework). This study employs the R programming language to conduct a systematic literature review in accordance with the “preferred reporting items for systematic reviews and meta-analysis” (PRISMA) guidelines.

Findings

The emerging discipline of HR analytics encompasses the potential to manage attrition and drive organisational performance enhancements effectively. The study of SCM-TBFO encompasses a multidimensional approach, incorporating diverse perspectives and analysing its complex aspects compared to various approaches. The School of thought includes the human capital theory, expectancy theory and resource-based view. The varied research contexts entail the USA, United Kingdom, China, France, Italy and India. Further, the methodologies adopted in the studies are artificial neural networking (ANN), regression, structure equation modelling (SEM) case studies and other theoretical studies. HR analytics and attrition triggers are data mining decision systems, forecasting for firm performance and employee satisfaction. The barriers include leadership styles, cultural adaptability and lack of analytic skills, data security and organisational orientation. The facilitators were categorised into data and technology-related facilitators, human resource policies and organisational growth and performance-related facilitators. The study's primary outcomes are technology adoption, effective HR policies, HR strategies, employee satisfaction, career and organisational expansion and growth.

Originality/value

The primary goal of the literature review is to provide a comprehensive overview of the current state of HR analytics and its impact on organisational performance, particularly in relation to attrition. Further, the study suggests that attrition, a critical organisational concern, can be effectively managed by strategically utilising HR analytics and empowering data-driven interventions that optimise performance and enhance overall organisational outcomes.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 21 March 2024

Nanda Kumar Karippur, Pushpa Rani Balaramachandran and Elvin John

This paper aims at identifying the key factors influencing the adoption intention of data analytics for predictive maintenance (PdM) from the lens of the…

Abstract

Purpose

This paper aims at identifying the key factors influencing the adoption intention of data analytics for predictive maintenance (PdM) from the lens of the Technology–Organization–Environment (TOE) framework in the Singapore Process Industries context. The research model aids practitioners and researchers in developing a holistic maintenance strategy for large-scale asset-heavy process industries.

Design/methodology/approach

The TOE framework has been used in this study to consider a wide set of TOE factors and develop a research model with the support of literature. A survey is undertaken and the structural equation modelling (SEM) technique is adopted to test the hypotheses of the proposed model.

Findings

This research highlights the significant roles of digital infrastructure readiness, security and privacy, top management support, organizational competence, partnership with external consultants and government support in influencing adoption intention of data analytics for PdM. Perceived challenges related to organizational restructuring and process automation are not found significant in influencing the adoption intention.

Practical implications

This paper reports valuable insights on adoption intention of data analytics for PdM with relevant implications for the various stakeholders such as the leaders and senior managers of process manufacturing industry companies, government agencies, technology consultants and service providers.

Originality/value

This research uniquely validates the model for the adoption of data analytics for PdM in the process industries using the TOE framework. It reveals the significant technology, organizational and environmental factors influencing the adoption intention and highlights the relevant insights and implications for stakeholders.

Details

Journal of Manufacturing Technology Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-038X

Keywords

Article
Publication date: 21 August 2023

Matloub Hussain, Mian Ajmal, Girish Subramanian, Mehmood Khan and Salameh Anas

Regardless of the diverse research on big data analytics (BDA) across different supply chains, little attention has been paid to exploit this information across service supply…

Abstract

Purpose

Regardless of the diverse research on big data analytics (BDA) across different supply chains, little attention has been paid to exploit this information across service supply chains. The healthcare supply chains, where supply chain operations consume the second highest expenditures, have not completely attained the potential gains from data analytics. So, this paper explores the challenges of BDA at various levels of healthcare supply chains.

Design/methodology/approach

Drawing on the resource-based view (RBV), this research explores the various challenges of big data at organizational and operational level of different nodes in healthcare supply chains. To demonstrate the links among supply chain nodes, the authors have used a supplier-input-process-output-customer (SIPOC) chart to list healthcare suppliers, inputs (such as employees) supplied and used by the main healthcare processes, outputs (products and services) of these processes, and customers (patients and community).

Findings

Using thematic analysis, the authors were able to identify numerous challenges and commonalities among these challenges for the case of healthcare supply chains across United Arab Emirates (UAE). An applicable exploration on organizational (Socio-technical) and operational challenges to BDA can enable healthcare managers to acclimate efficient and effective strategies.

Research limitations/implications

The identified common socio-technical and operational challenges could be verified, and their impacts on the sustainable performance of various supply chains should be explored using formal research methods.

Practical implications

This research advances the body of literature on BDA in healthcare supply chains in that (1) it presents a structured approach for exploring the challenges from various stakeholders of healthcare chain; (2) it presents the most common challenges of big data across the chain and finally (3) it uses the context of UAE where government is focusing on medical tourism in the coming years.

Originality/value

Originality of this work stems from the fact that most of the previous academic research in this area has focused on technology perspectives, a clear understanding of the managerial and strategic implications and challenges of big data is still missing in the literature.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 10 August 2023

Chenxiao Wang, Fangcheng Tang, Qingpu Zhang and Wei Zhang

The purpose of this study is to investigate the impact of corporate social responsibility (CSR) on innovation performance and examine the moderating role of social media strategic…

Abstract

Purpose

The purpose of this study is to investigate the impact of corporate social responsibility (CSR) on innovation performance and examine the moderating role of social media strategic capability and big data analytics capability. Specifically, the authors explore the effects of both external and internal CSR on innovation performance.

Design/methodology/approach

The authors collected data from 221 senior, middle and research and development (R&D) managers of high-tech firms in China, using a questionnaire survey with a six-month interval.

Findings

The empirical results show that both external and internal CSR positively influence innovation performance. Furthermore, social media strategic capability has a positive moderating effect on the relationship between CSR and innovation performance, while big data analytics capability moderates the relationship between external CSR and innovation performance.

Research limitations/implications

The data comes from high-tech firms in China, which may limit the generalizability and external validity of the findings. Future studies should replicate this study in other industries and types of organizations.

Practical implications

The study suggests that high-tech firms should engage in both external and internal CSR activities to promote innovation performance. Moreover, leveraging social media strategic capability and big data analytics capability can enhance innovation performance.

Originality/value

This study contributes to the literature on CSR outcomes by empirically exploring the effects of external and internal CSR on innovation performance, thus extending stakeholder theory. Additionally, by revealing the contingency effects of social media strategic capability and big data analytics capability, this study enriching the research on dynamic capabilities theory in the context of digital transformation.

Details

European Journal of Innovation Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1460-1060

Keywords

Article
Publication date: 22 March 2024

Kojo Kakra Twum and Andrews Agya Yalley

The use of innovative technologies by firm employees is a key factor in ensuring the competitiveness of firms. However, researchers and practitioners have been concerned about the…

Abstract

Purpose

The use of innovative technologies by firm employees is a key factor in ensuring the competitiveness of firms. However, researchers and practitioners have been concerned about the willingness of technology end users to use innovative technologies. This study, therefore, aims to determine the factors affecting the intention to use marketing analytics technology.

Design/methodology/approach

This study surveyed 213 firm employees. The quantitative data collected was analysed using partial least squares structural equation modelling.

Findings

The results reveal that performance expectancy, facilitating conditions, attitudes and perceived trust have a positive and significant effect on intentions to use marketing analytics. Effort expectancy, social influence and personal innovativeness in information technology were found not to predict intentions to use marketing analytics.

Practical implications

This study has practical implications for firms seeking to enhance the use of marketing analytics technology in developing countries.

Originality/value

This study contributes to the use of UTAUT, perceived trust, personal innovativeness and user attitude in predicting the intentions to use marketing analytics technology.

Details

Journal of Science and Technology Policy Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2053-4620

Keywords

Article
Publication date: 29 March 2024

Anil Kumar Goswami, Anamika Sinha, Meghna Goswami and Prashant Kumar

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers…

Abstract

Purpose

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

Design/methodology/approach

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

Findings

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

Research limitations/implications

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

Practical implications

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

Originality/value

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 31 January 2024

Shan Wang, Ji-Ye Mao and Fang Wang

Digital innovation requires organizations to reconfigure their information technology infrastructure (ITI) to cultivate creativity and implement fast experimentation. This…

Abstract

Purpose

Digital innovation requires organizations to reconfigure their information technology infrastructure (ITI) to cultivate creativity and implement fast experimentation. This research inquiries into ITI generativity, an emerging concept demoting a critical ITI capability for organizational digital innovation. More specifically, it conceptualizes ITI generativity across two dimensions—namely, systems and applications infrastructure (SAI) generativity and data analytics infrastructure (DAI) generativity—and examines their respective social and technical antecedents and their impact on digital innovation.

Design/methodology/approach

This research formulates a theoretical model to investigate the social and technical antecedents along with innovation outcomes of ITI generativity. To test this model and its associated hypotheses, a survey was administered to IT professionals possessing knowledge of their organization's IT architecture and digital innovation performance. The dataset, comprising responses from 140 organizations, was analyzed using the partial least squares technique.

Findings

Results reveal that both dimensions of ITI generativity contribute to digital innovation performance, with the effect of DAI generativity being more pronounced. In addition, SAI and DAI generativities are driven by social and technical factors within an organization. More specifically, SAI generativity is positively associated with the usage of a digital application services platform and IT human resources, whereas DAI generativity is positively linked to the usage of a data analytics services platform, data analytics services usability and data analytics human resources.

Originality/value

This research contributes to the literature on digital innovation by introducing ITI generativity as a crucial ITI capability and deciphering its role in digital innovation. It also offers useful insights and guidance for practitioners on how to build ITIs to achieve better digital innovation performance.

Article
Publication date: 4 September 2023

Hsiao-Ting Tseng, Shizhen (Jasper) Jia, Tahir M. Nisar and Nick Hajli

The advantages of applying big data analytics for organizations to boost innovation performance are enormous. By collecting and analysing substantial amounts of data, firms can…

Abstract

Purpose

The advantages of applying big data analytics for organizations to boost innovation performance are enormous. By collecting and analysing substantial amounts of data, firms can discern what works for their customer needs and update existing products while innovating new ones. Notwithstanding the evidence about the effects of big data analytics, the link between big data analytics and innovation performance is still underestimated. Especially in today's fast-changing and complicated environments, companies cannot simply take big data analytics as one innovative technical tool without fully understanding how to deploy it effectively.

Design/methodology/approach

This study tries to investigate this relationship by building on the knowledge absorptive capacity perspective. The authors conceptualized effective use of big data analytics tools as one general absorptive capacity rather than a simple technical element or skill. Specifically, effectively utilize big data analytics tools can provide values and insights for new product innovation performance in a turbulent environment. Using online survey data from 108 managers, the authors assessed their hypotheses by applying the structural equation modelling method.

Findings

The authors found that big data analytics capacity, which can be conceptualized as one absorptive capacity, can positively influence product innovation performance. The authors also found that environmental turbulence has strong moderation effects on these two main relationships.

Originality/value

These results establish big data analytics can be regarded as one absorptive capacity, which can positively boost an organization's innovation performance.

Details

Information Technology & People, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0959-3845

Keywords

Article
Publication date: 6 September 2023

Abeer M. Abdelhalim

This study aims to investigate the relationships between big data analytics, management accounting practices and corporate sustainability and, more precisely, the impact of the…

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Abstract

Purpose

This study aims to investigate the relationships between big data analytics, management accounting practices and corporate sustainability and, more precisely, the impact of the integration between big data analytics and management accounting on corporate sustainability performance development.

Design/methodology/approach

A qualitative case study approach is used in this study with multiple collecting data tools as in-depth interviews and observations, in addition to the content analysis used of the annual reports for the year 2021, of Almarai manufacturing corporate (one of the leaders of food and beverage manufacturing corporates in Saudi Arabia and other countries).

Findings

Research findings provide good insights about the significant impact of the effective integration between big data analytics and management accounting on corporate sustainability performance development, big data can assist management accounting to form corporate value-added strategies and activities.

Research limitations/implications

The study is limitedly applied to one manufacturing corporate as a study case; therefore, the findings cannot be generalized. Thus, future research can examine the association between the current study variables with wide-scale applications and with different approaches and in different contexts to enrich the findings. Moreover, future research may focus on the integration between big data analytics and management accounting reports in the meta-verse environment to explore the benefits that corporates could gain from the features and capabilities of meta-verse technology.

Originality/value

There is a research gap regarding the impact of the integration between big data analytics and management accounting practices on corporate sustainability development, as most of the previous studies focused on two variables only of the current study variables; therefore, this study tries to investigate and give important insights about it.

Details

Journal of Financial Reporting and Accounting, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1985-2517

Keywords

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