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The information systems (IS) literature has indicated the importance of data analytics capabilities (DAC) in improving business performance in organizations. The literature has also highlighted the roles of organizations’ data-related resources in developing their DAC and enhancing their business performance. However, little research has taken resource quality into account when studying DAC for business performance enhancement. Therefore, the purpose of this paper is to understand the impact of resource quality on DAC development for business performance enhancement.

We studied DAC development using the resource-based view and the IS success model based on empirical data collected via 19 semi-structured interviews.

Our findings show that data-related resource (including data, data systems, and data services) quality is vital to the development of DAC and the enhancement of organizations’ business performance. The study uncovers the factors that make up each quality dimension, which is required for developing DAC for business performance enhancement.

Using the resource quality view, this study contributes to the literature by exploring the role of data-related resource quality in DAC development and business performance enhancement.

The aim of this study is to empirically investigate the impact of marketing analytics capability on business performance from the perspective of RBV theory.

This study used a survey method to gather information from 225 food processing SMEs registered with the Ghana Enterprise Agency (GEA) in Ghana’s eastern region. A structural equation modeling (SEM) path analysis was used to assess the impact of marketing analytics capability (MAC) on the performance of SMEs.

The results of the study show that MAC significantly and positively affect the financial performance (FP), customer performance (CF), internal business process performance (IBPP) and learning and growth performance (LGP) of Ghanaian SMEs. The findings of this study also illustrated the significance of MAC determinants, including marketing analytics skills (MAS), data resource management (DRM) and data processing capabilities (DPC), in achieving SME success in Ghana.

The research’s conclusions give RBV theory strong credence. The results of this study also provide credence to previous research finding that SMEs should view MAC and its determinants (i.e. DRM, DPC, MAS) as a crucial strategic capability to improve their performance (i.e. FP, CF, IBPP, LGP). With regard to its contribution, this study broadens the body of knowledge on MAC and SME performance, particularly in the context of an emerging economy.

Though business analytics capability continues to attract considerable industrial and scholarly attention, its holistic performance implications, especially in the post-COVID-19 period, have not been fully understood. Thus, there have been calls for a full understanding of the implications of BAC for achieving holistic, sustainable outcomes among firms. This study therefore examines the influence of BAC on the three dimensions of sustainable performance. We also proposed the mediating role of circular economy implementation.

We tested the proposed model using survey data from 246 managers of manufacturing firms in Ghana. Partial least squares structural equation modelling was employed to validate the model.

Our findings showed that BAC significantly enhances both sustainable performance and circular economy implementation. We also found a significant association between CEI and sustainable performance. We further found significant partial mediation of CEI in the BAC sustainable performance nexus.

Our study offers thoughtful insights for managers, policymakers and the academic community that firms should simultaneously implement circular models alongside building analytics competencies in the quest to achieve balanced performance outcomes.

To the best of our knowledge, our study is among the very few attempts to understand the mechanism that channels the benefits of BAC for a holistic, sustainable outcome.

The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components. Data analytics, HRM and strategic business require empirical investigations and how to over come HR data analytics implementation issues.

A semi-systematic methodology for its evaluation allows for a more complete examination of the literature that emerges theoretical framework and a structured survey questionnaire for quantitative data collection from IT sector personnel. SPSS analyses data.

Future research is essential for organisations to exploit HR data analytics’ performance-enhancing potential. Data analytics should complement human judgment, not replace it. This paper details these transitions, the important contributions to theory and practice and future research.

Data analytics has grown rapidly and might make HRM practices faster, more efficient and data-driven. HR data analytics may improve strategic business. HR data analytics on employee retention, engagement and organisational success is insufficient. HR data analytics may boost performance, but there is limited proof. The authors do not know how HRM data analytics influences firms and employees.

Data analytics offers HRM new opportunities, along with technical and ethical challenges. This study makes a significant contribution to HR data analytics, evidence-based practice and strategic business literature. In addition to estimating turnover risk, identifying engagement factors and planning interventions to increase retention and engagement, HR data analytics can also estimate the risk of employee attrition.

Previous studies focus on the direct effects of marketing analytics on entrepreneurial performance, but few explore the underlying mechanisms. Drawing on affordance theory, this study explores pathways through new product innovation (NPI) for the effects of marketing analytics on business performance. NPI is a market-based innovation concept comprising customer- and competitor-driven NPD and incremental innovation.

Using survey data collected from UK-based entrepreneurial firms operating in the IT and telecoms industries, we apply confirmatory factor analysis and a sequential structural equation model to test the mediating role of NPI in the effect of marketing analytics on market performance and financial performance.

The results show that marketing analytics enhances business performance through competitor-driven but not customer-driven NPD. Although using marketing analytics to generate customer knowledge for existing product innovation may enhance market performance, this positive effect becomes negative when competitor-driven NPD is undertaken to improve existing product innovation.

This study makes significant contributions to the innovation and NPD literature. It delves deeper into the existing view on the positive contributions of customer engagement to business value creation, revealing the significance of competitor knowledge to enhance business performance through marketing analytics, particularly in the context of IT and telecoms entrepreneurial firms.

This study investigates the relationships between data governance (DG), business analytics capabilities (BAC), and decision-making performance (DMP), with a focus on the mediating effects of big data literacy (BDL) and data analytics competency (DAC).

The study was conducted with 178 experienced managers in public service organizations, using a quantitative approach. Structural equation modeling (SEM) and mediation tests were employed to analyze the data.

The findings reveal that DG and BDL are critical antecedents for developing analytical capabilities. Big data literacy mediates the relationship between DG and BAC, while BAC mediates the relationship between DG and DMP. Furthermore, DAC mediates the relationship between BA capabilities and DMP, explaining most of the effect of BAC on DMP.

These results highlight the importance of DG in fostering BDL and analytical skills for improved decision-making in organizations.

By prioritizing DG practices that promote BDL and analytical capabilities, organizations can leverage business analytics to enhance decision-making.

The purpose of this study was to analyze the effects of social media analytics on firm performance using a sample of small and medium enterprises (SMEs) in Thailand. This study also investigated whether entrepreneurial orientation (EO) moderated the effects of social media analytics on firm performance.

This study used SMEs listed in the Department of Business Development of Thailand as the sampling frame. Probability sampling was used to draw the sample. A questionnaire survey was used to collect data from 334 firms. The data were analyzed using partial least squares structural equation modeling.

The results supported the positive association between social media analytics practices on firm performance. Moreover, this study found that EO moderated this association significantly. In particular, the positive association between social media analytics practices on firm performance was higher for firms that exhibit a high EO than those that exhibit a low EO. This result indicated that firms that implement social media analytics practices achieved higher performance when they exhibited a high EO.

Social media data analytics should be implemented to strengthen the technological competence of firms. Moreover, firms should integrate EO practices into their implementation of social media analytics to increase their ability to generate substantial improvements in their strategic implementation, thereby enabling them to gain sustainable competitiveness in their market.

Because SMEs are the driving force for economic growth and development in Thailand, their ability to achieve higher performance when they effectively integrate EO practices into their implementation of social media data analytics could be beneficial for the sustainable development of Thailand, especially in the current data-driven era.

The result that EO moderates the effect in enhancing social media analytics practices’ influence on firm performance provides new knowledge that extends the boundary of research on this topic. The authors provided a theoretical explanation to clarify the way the implementation of social media analytics practices should be integrated with EO to increase the level of performance that firms achieve from such practices.

Although it is commonly agreed that prescriptive analytics can benefit organizations by enabling better decision-making, the deployment of prescriptive analytics tools can be challenging. Previous studies have primarily focused on methodological issues rather than the organizational deployment of analytics. However, successful deployment is key to achieving the intended benefits of prescriptive analytics tools. Therefore, this study aims to identify the enablers of successful deployment of prescriptive analytics.

The authors examine the enablers for the successful deployment of prescriptive analytics through five organizational case studies. To provide a comprehensive view of the deployment process, each case includes interviews with users, managers and top management.

The findings suggest the key enablers for successful analytics deployment are strong leadership and management support, sufficient resources, user participation in development and a common dialogue between users, managers and top management. However, contrary to the existing literature, the authors found little evidence of external pressures to develop and deploy analytics. Importantly, the success of deployment in each case was related to the similarity with which different actors within the organization viewed the deployment process. Furthermore, end users tended to highlight user participation, skills and training, whereas managers and top management placed greater emphasis on the importance of organizational changes.

The results will help practitioners ensure that key enablers are in place to increase the likelihood of the successful deployment of prescriptive analytics.

In recent times, the field of corporate intelligence has gained substantial prominence, employing advanced data analysis techniques to yield pivotal insights for instantaneous strategic and tactical decision-making. Expanding beyond rudimentary post observation and analysis, social media analytics unfolds a comprehensive exploration of diverse data streams encompassing social media platforms and blogs, thereby facilitating an all-encompassing understanding of the dynamic social customer landscape. During an extensive evaluation of social media presence, various indicators such as popularity, impressions, user engagement, content flow, and brand references undergo meticulous scrutiny. Invaluable intelligence lies within user-generated data stemming from social media platforms, encompassing valuable customer perspectives, feedback, and recommendations that have the potential to revolutionize numerous operational facets, including supply chain management. Despite its intrinsic worth, the actual business value of social media data is frequently overshadowed due to the pervasive abundance of content saturating the digital realm. In response to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS).

Leveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward. Specifically tailored to the domain of marketing, the framework delineates a practical methodology for extracting invaluable insights from the vast expanse of social data. This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

To substantiate the efficacy of the EJDSS, a detailed case study centered around reverse logistics resource recycling is presented, accompanied by experimental findings that underscore the system’s exceptional performance. The study showcases remarkable precision, robustness, F1 score, and variance statistics, attaining impressive figures of 83.62%, 78.44%, 83.67%, and 3.79%, respectively.

This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

This research investigates the impact of business analytics (BA) on corporate entrepreneurship (CE) and open innovation (OI), considering the moderated mediation analysis in the context of Iran as a developing country. The study was conducted in various industries, including food, chemicals, agriculture, automobile, and service industries, with 207 observations.

Through an in-depth review of the extant literature a conceptual model was developed and the proposed hypotheses were tested using Structural Equation Modeling technique (PLS-SEM).

The results indicate that business analytics has significant effects on corporate entrepreneurship and open innovation. Open innovation has a significant effect on corporate entrepreneurship, with open innovation serving as a suitable mediator. Furthermore, the moderated mediation analysis shows the positive impact of Business Analytics on Open Innovation-Corporate Entrepreneurship relationship.

As this study was conducted in Iran, one of the main limitations can be attributed to the specific characteristics of the country which may affect how and how much the variables influence each other.

The study highlights the importance of promoting Open Innovation in organizations and utilizing Business Analytics to make strategic decisions and foster innovation in entrepreneurial activities.

This study fills the gap in the literature by exploring how BA contributes to corporate entrepreneurship of the Iranian organizations in various industries, given open innovation as a mediator under dynamic market conditions.