Data-driven Culture Research Articles

Information Quality of Business Intelligence Systems: A Maturity-based Assessment

Background: The primary role of a Business Intelligence (BI) system is to provide information to decision-makers within an organization. Moreover, it is crucial to acknowledge that the quality of this information is of greatest significance. Several studies have extensively discussed the importance of information quality in information systems, including BI. However, there is relatively little discussion on the factors influencing 'Information quality”. Objective: This study aimed to address this literature gap by investigating the determinants of BI maturity that impacted information quality. Methods: A maturity model comprising three dimensions was introduced, namely Data quality, BI infrastructure, and Data-driven culture. Data were collected from 84 companies and were analyzed using the SEM-PLS approach. Results: The analysis showed that maturity had a highly positive influence on Information Quality, validating the relevance of the three proposed determinant factors. Conclusion: This study suggested and strongly supported the importance and relevance of Data quality, BI infrastructure, and Data-driven culture as key dimensions of BI maturity. The robust statistical relationship between maturity and information quality showed the effectiveness of approaching the systems from a maturity perspective. This investigation paved the way for exploring additional dimensions that impact Information quality. Keywords: BI infrastructure, BI maturity, Data-driven culture, Data quality, Information quality.

Pipeline Design for Data Preparation for Social Media Analysis

In a data-driven culture, in which analytics applications are the main resources for supporting decision-making, the use of high-quality datasets is mandatory to minimize errors and risks. For this reason, data analysis tasks need to be preceded by a data preparation pipeline. The design of such a pipeline is not trivial: the data analyst must carefully choose the appropriate operations considering several aspects. This is often performed by adopting a trial-and-error approach that does not always lead to the most effective solution. In addition, extracting information from social media poses specific problems due to the need to consider only posts relevant for the analysis, for its dependence from the context being considered, for its multimedia contents, and for the risk of filtering out informative posts with automatic filters. In this article, we propose a systematic approach to support the design of pipelines that are able to effectively extract a relevant dataset for the goal of the analysis of data from social media. We provide a conceptual model for designing and annotating the data preparation pipeline with quality and performance information, thus providing the data analyst preliminary information on the expected quality of the resulting dataset in a context-aware manner. The generation of metadata related to the processing tasks has been recognized as essential for enabling data sharing and reusability. To this aim, the dataset resulting from the pipeline application is automatically annotated with provenance metadata to get a detailed description of all the activities performed by the pipeline on them. As a case study, we consider the design of a pipeline for creating datasets of images extracted from social media in order to analyze behavioural aspects during COVID-19.

Assessing Maturity in Data-Driven Culture

Research on assessing a group's maturity in data-driven culture is rare and fragmented. This article investigates how maturity in data-driven culture can be assessed from a historical perspective. A case study was done on how the Education Council evolved in analytics maturity and as a group during 2014-2023. The assessment showed that the Education Council experienced both successful progression of group development and usage of analytics, as well as regression in group development and analytics usage. The practical implications of the findings are that group leaders need to be aware of the interplay between analytics usage and group development when planning to improve their group's maturity in data-driven culture.

Artificial Intelligence in Human Resource Management: Advancements, Implications and Future Prospects

The present condition, challenges, and potential applications of artificial intelligence (AI) in human resource management (HRM) are all explored in this survey article. As an innovation, artificial intelligence (AI) has the potential to completely revolutionize several facets of human resource management (HRM). Examining the usage of AI-powered tools and systems in different HR processes, the present situation with AI in HRM is examined. These encompass learning and development, performance management, employee engagement, and recruiting. The use of AI algorithms and machine learning approaches to automate regular HR operations, analyze vast amounts of employee data, and provide insightful data to aid decision-making is addressed in this article. However, integrating AI into HRM also poses a number of difficulties that must be resolved. Bias, privacy issues, and transparency are just a few of the ethical and legal ramifications of using AI in decision-making processes that are discussed in this survey. The study emphasizes how accountability and fairness must be maintained in AI systems by responsible design, oversight, and periodic evaluation. With an emphasis on job displacement and workforce reorganization, the possible influence of AI on the human workforce is also explored. To effectively traverse this change, strategies including work role redefinition, employee up skilling, and establishing a collaborative atmosphere between humans and AI are suggested. The possible advantages and breakthroughs that AI might bring to HRM practices are highlighted as the future perspectives of AI in HRM are examined. As new applications for AI in HRM, sentiment analysis, predictive analytics, intelligent decision support, and personalized employee experiences are all highlighted. In order to fully realize the promise of AI in HRM, the study underlines the significance of data infrastructure, data governance frameworks, and a data-driven culture. Overall, this survey study offers an in-depth review of the existing situation, difficulties, and prospects for AI in HRM. It aggregates current information, identifies research gaps, and gives practitioners and scholars new perspectives on how AI will fundamentally alter the way HRM activities are carried out in the future.

Data-driven decision making in advanced manufacturing Systems: modeling and analysis of critical success factors

ABSTRACT Data-driven decision making (DDDM) in advanced manufacturing systems (AMS) is the use of data to make smart decisions that improve manufacturing operations. Companies can make themselves more competitive, cut costs, and improve their production by using data analytics. The investigation of critical success factors aids companies in identifying vital areas that demand attention for the implementation of DDDM in AMS. This comprehension enables companies to devise effective strategies for the successful adoption of DDDM within AMS. In this research, twelve critical success factors that affect the use of DDDM in AMS were discovered and statistically analysed using an integrated methodology of ISM, MICMAC, and DEMATEL to create a hierarchical model. This research paper suggests that companies should focus on developing a skilled workforce and creating a data-driven culture to successfully adopt DDDM in AMS. Additionally, the findings highlight the importance of top management support and government initiatives in promoting the adoption of DDDM in manufacturing.

Man vs the machine in the struggle for effective text anonymisation in the age of large language models

The collection and use of personal data are becoming more common in today’s data-driven culture. While there are many advantages to this, including better decision-making and service delivery, it also poses significant ethical issues around confidentiality and privacy. Text anonymisation tries to prune and/or mask identifiable information from a text while keeping the remaining content intact to alleviate privacy concerns. Text anonymisation is especially important in industries like healthcare, law, as well as research, where sensitive and personal information is collected, processed, and exchanged under high legal and ethical standards. Although text anonymisation is widely adopted in practice, it continues to face considerable challenges. The most significant challenge is striking a balance between removing information to protect individuals’ privacy while maintaining the text’s usability for future purposes. The question is whether these anonymisation methods sufficiently reduce the risk of re-identification, in which an individual can be identified based on the remaining information in the text. In this work, we challenge the effectiveness of these methods and how we perceive identifiers. We assess the efficacy of these methods against the elephant in the room, the use of AI over big data. While most of the research is focused on identifying and removing personal information, there is limited discussion on whether the remaining information is sufficient to deanonymise individuals and, more precisely, who can do it. To this end, we conduct an experiment using GPT over anonymised texts of famous people to determine whether such trained networks can deanonymise them. The latter allows us to revise these methods and introduce a novel methodology that employs Large Language Models to improve the anonymity of texts.

Discovering research data management trends from job advertisements using a text-mining approach

In today’s data-driven culture, research data management (RDM) is essential for the research community. The demand for reusing research datasets is a challenging and diverse process for the scientific community. Despite this, it is essential in RDM to discover trends and themes using text mining, which is scarce. The purpose of this study is to employ text mining to discover insights from job advertisements associated with RDM profiles, which collected 810 advertisements. We found RDM-related patterns using latent Dirichlet allocation (LDA) and identified three key contexts. The first is ‘research services in libraries’, with the topics of research services, research information, research universities, collection processes and library services. The second context is ‘research data’, which includes RDM, business data, university data, research data, health research, science research, social science research, data centres, data services, statistical software, digital scholarship and digital preservation. The third context is ‘workplace environment’, and the topics are leadership, work development and scientific position. Job title normalisation reveals names such as ‘data librarian’, ‘librarian’, ‘director’, ‘data curator’, ‘data manager’, ‘research data librarian’, ‘data specialist’ and ‘data officer’ are frequently employed. Focusing on titles with a single or double occurrence is new and interesting for developing nations. Reputable institutions such as Harvard, Stanford and the Massachusetts Institute of Technology, as well as countries such as the United States, the United Kingdom, Canada and Germany, are the major participants in RDM practises and services. This discovery will assist higher education institutions, RDM stakeholders, which aid in the formulation of curriculum, and job seekers to familiarise themselves with the themes.

Utilizing Big Data Analytics and Business Intelligence for Improved Decision-Making at Leading Fortune Company

The present study evaluates Walmart’s existing big data analytics with business intelligence techniques, accentuating their strengths and weaknesses, and suggests improvements for implementation and maintenance through the literature review of the scholarly journals addressing similar topics. Big data analytics is receiving loads of attention globally in the business environment within every sector of the economy. Incorporating the job plan as an additional input component in their models would be beneficial for Walmart to improve the precision and appropriateness of their data analysis and decision-making procedures. Walmart is a company that heavily invests in utilizing big data to improve its operations; this includes optimizing in-store experiences and predicting product trends. Scholarly articles emphasize the importance of advanced data analytics tools like MapReduce and Apache Spark for effective big data strategies. Social media content influences engagement and sentiment. Social network data aids sales forecasting but presents challenges. Big data analytics with business intelligence enhances performance and decision-making. Walmart's success in big data analytics relies on a data-driven culture but faces security challenges. Many Fortune 1000 companies adopt innovative solutions to improve performance and customer experiences but require significant resources. Embracing big data analytics with business intelligence remains a compelling investment for sustaining a competitive edge. Walmart's success in big data analytics is due to a data-driven culture and advanced infrastructure, including the Data Café.

Beyond Digital Data and Information Technology: Conceptualizing Data-Driven Culture

Abstract Background: The role of a data-driven culture in improving organizational performance is widely recognized, but its conceptual definition lacks uniformity, leading to the existence of various constructs. This paper proposes a guiding framework for a data-driven culture, aiming to foster a unified understanding that aids both researchers and practitioners in the information systems (IS) field. Method: Adopting a qualitative research approach, this study conducts a systematic literature review to discern the breadth and depth of data-driven culture as portrayed in previous works. Alongside this, ten interviews were carried out with professionals well-versed in the application of data-driven strategies. Results: The study uncovers the multifaceted nature of a data-driven culture, highlighting its influence on decision-making practices within organizations. It identifies a range of characteristics relevant to the construct and consolidates these into an integrative framework, thereby developing a conceptual definition for data-driven culture. Conclusion: The paper contributes to the IS field by providing a framework that illuminates the concept of data-driven culture. This new understanding aids researchers in consistently theorizing the same phenomenon, supports the development of refined metrics for assessing data-driven culture, and paves the way for future research in this area. For practitioners, this framework delineates the characteristics of a data-driven culture and their interplay, enabling a more informed approach to cultural change efforts. Moreover, it highlights the importance of acknowledging the wider cultural context, and provides mechanisms to balance the emphasis on tools and values.

Exploring the Effects of Data-Driven Hospital Operations on Operational Performance From the Resource Orchestration Theory Perspective

In the big data era, managing data-driven hospital operations have become one of the most important tasks for healthcare executives, increasing responsiveness to exceptional disruptions such as those caused by the COVID-19 pandemic. However, they are still facing the challenges of how best to orchestrate the digital medical resources for improving operational performance such as cost, delivery, and quality. Therefore, drawing upon resource orchestration theory, this article investigates how hospitals orchestrate data-driven culture (DDC) and digital technology orientation (DTO) to develop big data analytics capability (BDAC) for operational performance improvement. Survey data were collected from 105 hospitals in China and analyzed using structural equation modeling and ordinary least square regression. The results show that DDC has a significant positive impact on DTO. More interestingly, there is no significant interaction effect between DDC and DTO, indicating that DDC and DTO affect BDAC independently, and not synergistically. The results further reveal that BDAC fully mediates the DTO–operational performance relationship. The findings offer useful and timely guidance on how healthcare executives can manage data-driven hospital operations to improve operational performance during and post the COVID-19 pandemic.

CUSTOMER BEHAVIOR ANALYSIS THROUGH DATA ANALYTICS IN THE BANGLADESHI RETAIL INDUSTRY

This qualitative study aims to investigate the state of customer behavior analysis in the Bangladeshi retail industry, with a particular focus on the role of data analytics. The research seeks to explore the current practices, challenges, opportunities, and best strategies employed by retailers in understanding and responding to customer behavior. The Bangladeshi retail industry has witnessed substantial growth driven by factors such as economic development, urbanization, and evolving consumer preferences. In this evolving landscape, retailers increasingly rely on data analytics to gain valuable insights into customer behavior, optimize business operations, and enhance customer experiences. Understanding customer behavior is vital for retailers to tailor their strategies to meet the ever-changing needs and preferences of Bangladeshi consumers. This study employs qualitative research methods, including in-depth interviews with key stakeholders in the Bangladeshi retail industry. The sample size comprises 40 participants, and data analysis involves a thematic approach. The study explores the current state of customer behavior analysis, challenges, opportunities, best practices, and strategies related to data analytics within the retail sector. The findings reveal a growing integration of data analytics in customer behavior analysis, with retailers utilizing various data sources such as point-of-sale data, online transactions, and customer surveys. Challenges include data security concerns and the need for skilled data analysts, while opportunities encompass improved customer retention, enhanced personalization, and more effective marketing campaigns. Best practices emphasize fostering a data-driven culture, collaborating with external data analytics experts, and continuous staff training. The study’s insights have implications for the Bangladeshi retail industry, highlighting the transformative potential of data analytics in adapting to evolving customer preferences and remaining competitive. Recommendations include clear objective setting, investment in data security measures, and the pursuit of external expertise when needed.

Exploring the Relationship between Big Data Analytics Capability and Organization’s Strategic Intent: Mediating Role of Environmental Scanning

Big data analytics has attracted significant scholarly attention over the past few years due to its critical role in directing organizational decision making. Big data analytics capabilities (BDAC) are increasingly being suggested to be of great help in providing competitive advantages to the firms across industrial settings. Although, prior research has examined the influence of BDAC from a technological perspective; there is a lack of a research from a broader strategic perspective. Correspondingly, this study builds on dynamic capabilities view to propose a conceptual model that investigates the direct and indirect effects between three dimensions of BDAC on environmental scanning and organization’s strategic intent. This study adopted the questionnaire-based survey method to collect the data from 179 senior managers of pharmaceutical companies in Egypt. The results revealed that only the technological dimension of BDAC significantly affected the strategic intent only through supporting data-driven culture and organizational learning. The results also reveal that BDAC is positively associated with environmental scanning, which consequently affects strategic intent. Our results further reveal that environmental scanning mediates the relationship between BDAC and organization’s strategic intent. The paper concludes by offering some valuable theoretical and practical implications.

How does information technology capabilities affect business sustainability? The roles of ambidextrous innovation and data‐driven culture

This research investigates explicitly how Information technology capability (ITC) can be tailored to achieve business sustainability via ambidextrous innovation in a turbulent environment. Moreover, the moderating factor of a data‐driven culture (DDC) is also being investigated. The research model was built on the theoretical foundations of the resource‐based view (RBV), dynamic capabilities view and existing IT capabilities literature. Through a close‐ended survey questionnaire from approximately 254 IT personnel in managerial positions working in the manufacturing industry, partial least squares‐structural equation modelling‐artificial neural network (PLS‐SEM‐ANN) is applied. Notably, the result indicated that ITC positively influences IT‐enabled dynamic capabilities (ITDC), which positively impacts ambidextrous innovations and helps manufacturing firms achieve business sustainability. Meanwhile, the moderating role of DDC between ITDC and exploitative innovation was also found. The results showed that ANN models A, B, C and D are consistent for both analysis. This study provides several novel additions to the literature on operations management and information systems (IS) researchers. IS researchers should focus on leveraging ITC to enable and support organisational capabilities rather than the direct effects of IT expenditures. Additionally, the importance of a DDC has seldom been explored together previously. Thus, this study fills gaps in existing knowledge, along with significant insights, specifically for future IT implementation. As for practical implications, this study provides corporate leaders and executives with insights about how to leverage their resources and customise their ITC in an unpredictable and volatile business environment.

Green human resources management, green innovation and circular economy performance: the role of big data analytics and data-driven culture

This study aimed to illustrate the impact of green human resources management on circular economy performance; the mediator role of green innovation; and the moderator roles of big data analytics and data-driven culture. A total of 438 survey questionnaires were collected from small and medium-sized enterprises from the Pakistani textile sector and evaluated through partial least square-structural equation modelling. The study’s outcomes revealed that green human resources management significantly impacts circular economy performance. Similarly, green innovation and big data analytics mediated and moderated between green human resources management and circular economy performance. Nevertheless, data-driven culture is needed to moderate between green human resources management and circular economy performance. The study presented a unique and novel idea of green human resources management concerning circular economy performance – the best solution for sustainable environmental fortification in the current decade while enhancing circular economy performance.

The effects of analytics capability and sensing capability on operations performance: the moderating role of data-driven culture.

Studies indicate that organizational capability is a key factor in operational performance, and that both sensing and analytics capabilities have a significant influence on operational performance. This study develops a framework to examine the impact of organizational capability on operational performance, with a specific focus on the implementation of sensing and analytics capabilities. We combine strategic fit theory, the dynamic capability view, and the resource-based view to examine how micro, small, and medium enterprises (MSMEs) strategically integrate a data-driven culture (DDC) with their organizational capabilities to enhance operational performance. We carry out empirical research to investigate whether a DDC moderates the influence of organizational capability on operational performance. Structural equation modeling of survey data from 149 MSMEs reveals that both sensing and analytics capabilities have a positive impact on operational performance. The results also suggest that a DDC positively moderates the influence of organizational capability on operational performance. We discuss the theoretical and managerial implications of our findings, the limitations of the study, and opportunities for further research.

‘It’s a generational thing, really’. Understandings of sexual rights in a digital age

Deriving from a large-scale research project on intimacy in data-driven culture in Finland, I build on a sub-study examining Finland’s presumed sexual digital divide between generations. I analysed in-depth, semi-structured research discussions with six social media users who actively participated in the online discussion following the posting of a video on TikTok by the National Board of Investigation on the potential risks and dangers of sending nude images online. I wanted to know why this particular ‘awareness-raising video’ gained so much negative feedback from its young audiences on TikTok and other social media platforms. By using the idea of ‘generational sexualities’, I focus on the shared social imaginary the research participants seem to have on the sexual and digital divide between generations. As a result of my analysis, I propose that the idea of a generational gap regarding the digital modes of sexual engagement is a culturally and socially shared narrative contributing to a disconnective effect between the generations in sexual matters. Furthermore, the analysis of my research materials reveals that young people’s ‘full sexual rights’ operate as a specific generational narrative that organises and structures my research participants’ perspectives of what is inclusive and socially just sexual education. The educational efforts must go beyond risk and harm as ‘no-sexting’ educational materials may contribute to sexual discrimination and marginalisation, increase social inequality and negatively impact young people’s sexual wellbeing.

Tech-Business Analytics – a Review-based New Model to Improve the Performances of Various Industry Sectors

Purpose: Integration of ICCT underlying technologies and big data technology to develop a new kind of Business analytics that can be used to solve semi-structured and unstructured problems of various industry sectors i.e., primary, secondary, tertiary, and quaternary industry sectors. The new study is known as Tech-business analytics (TBA). The goal of this study is to better understand the idea of TBA and how it influences a company's innovation outcomes. Design/Methodology/Approach: The originality of business analytics products/services in many industries is based on how business analytics is directly influenced by data-driven culture, but product/service importance is influenced indirectly by environmental scanning, which is further influenced by the usage of ICCT underlying technologies. Through a comprehensive review, analysis of the existing state, anticipating ideal/desired status, identifying research gaps, and analysing of research objectives in business analytics, this have developed a new concept of Tech-Business Analytics in this work. The paper also examines the importance of Business Analytics (BA) and how to predict the importance and applications of projected business analytics in future business sectors using the ABCD analytical framework. Findings/Result: This review based a new concept of integrated technology that combines ICCT underlying technologies with big data leads to a new model of tech-business analytics for improving the performance of different industry sectors. With all the resources, templates, technologies, opportunities, and capabilities of integrating Data science with other ICCT underlying technologies, Tech-business analytics will a paradigm shifter with a lot of potentials in solving industrial problems. Originality/Value: The new model of tech-business analytics is developed which is a review-based new concept opportunity for improving Industry Performance in Various Industries. A generic architecture is also developed, which looks at Tech Business Analytics in Primary, Secondary, Tertiary, and Quaternary industry sectors and is useful for research for technical efficiency improvement purposes. Paper Type: Exploratory research.

The Impact of Business Intelligence and Analytics Adoption on Decision Making Effectiveness and Managerial Work Performance

Business Intelligence and Analytics systems have the capability to enable organizations to better comprehend their business and to increase the quality of managerial decisions, and consequently improve their performance. Recently, organizations have embraced the idea that data becomes a core asset, and this belief also changes the culture of the organization; data and analytics now determine a data-driven culture, which makes way for more effective data-driven decisions. To the best of our knowledge, there are few studies that investigate the effects of BI&A adoption on individual decision-making effectiveness and managerial work performance. This paper aims to contribute to bridging this gap by providing a research model that examines the relationship between BI&A adoption and manager’s decision-making effectiveness and then his individual work performance. The research model also theorizes that a data-driven culture promotes the BI&A adoption in the organization. Using specific control variables, we also expect to observe differences between different departments and managerial positions, which will provide practical implications for companies that work on BI&A adoption.

Big data analytics capabilities and supply chain performance: testing a moderated mediation model using partial least squares approach

PurposeIn this study, the authors investigate the effect of big data analytics capability (BDAC) on supply chain performance (SCP) to assess the mediating effect of supply chain innovation (SCI) and the moderating effect of a data-driven culture (DDC).Design/methodology/approachThe authors collected the primary data through an online questionnaire survey from the manufacturing sector operating in Jordan. The authors used 420 samples for the final data analysis, which the authors performed via partial least squares structural equation modelling using SmartPLS 3.3.9 software.FindingsThe results indicate that BDAC has a strong relationship with SCI and SCP. SCI shows a positive relationship with SCP as well as a mediating effect on SCI. The authors confirmed that DDC moderated the relationship between SCI and SCP.Originality/valueThe authors developed a conceptual and empirical model to investigate the relationship between BDAC, SCI, DDC and SCP. The authors contributed new theoretical and managerial insights that add value to the supply chain management literature through testing the moderated-mediated model of these constructs in Jordan’s manufacturing sector.

Antecedents of Business Analytics Adoption and Impacts on Banks’ Performance: The Perspective of the TOE Framework and Resource-Based View

Aim/Purpose: This study utilized a comprehensive framework to investigate the adoption of Business Analytics (BA) and its effects on performance in commercial banks in Jordan. The framework integrated the Technological-Organizational-Environmental (TOE) model, the Diffusion of Innovation (DOI) theory, and the Resource-Based View (RBV). Background: The recent trend of utilizing data for business operations and decision-making has positively impacted organizations. Business analytics (BA) is a leading technique that generates valuable insights from data. It has gained considerable attention from scholars and practitioners across various industries. However, guidance is lacking for organizations to implement BA effectively specific to their business contexts. This research aims to evaluate factors influencing BA adoption by Jordanian commercial banks and examine how its implementation impacts bank performance. The goal is to provide needed empirical evidence surrounding BA adoption and outcomes in the Jordanian banking sector. Methodology: The study gathered empirical data by conducting an online questionnaire survey with senior and middle managers from 13 commercial banks in Jordan. The participants were purposefully selected, and the questionnaire was designed based on relevant and well-established literature. A total of 307 valid questionnaires were collected and considered for data analysis. Contribution: This study makes a dual contribution to the BA domain. Firstly, it introduces a research model that comprehensively examines the factors that influence the adoption of BA. The proposed model integrates the TOE framework, DOI theory, and RBV theory. Combining these frameworks allows for a comprehensive examination of BA adoption in the banking industry. By analyzing the technological, organizational, and environmental factors through the TOE framework, understanding the diffusion process through the DOI theory, and assessing the role of resources and capabilities through the RBV theory, researchers and practitioners can better understand the complex dynamics involved. This integrated approach enables a more nuanced assessment of the factors that shape BA adoption and its subsequent impact on business performance within the banking industry. Secondly, it uncovers the effects of BA adoption on business performance. These noteworthy findings stem from a rigorous analysis of primary data collected from commercial banks in Jordan. By presenting a holistic model and delving into the implications for business performance, this research offers valuable insights to researchers and practitioners alike in the field of BA. Findings: The findings revealed that various technological (data quality, complexity, compatibility, relative advantage), organizational (top management support, organizational readiness), and environmental (external support) factors are crucial in shaping the decision to adopt BA. Furthermore, the study findings demonstrated a positive relationship between BA adoption and performance outcomes in Jordanian commercial banks. Recommendations for Practitioners: The findings suggest that Jordanian commercial banks should enforce data quality practices, provide clear standards, invest in data quality tools and technologies, and conduct regular data audits. Top management support is crucial for fostering a data-driven decision-making culture. Organizational readiness involves having the necessary resources and skilled personnel, as well as promoting continuous learning and improvement. Highlighting the benefits of BA helps overcome resistance to technological innovation and encourages adoption by demonstrating improved decision-making processes and operational efficiency. Furthermore, external support is crucial for banks to adopt Business Analytics (BA). Banks should partner with experienced vendors to gain expertise and incorporate best practices. Vendors also provide training and technical support to overcome technological barriers. Compatibility is essential for optimal performance, requiring managers to modify workflows and IT infrastructure. Complexity, including data, organizational, and technical complexities, is a major obstacle to BA adoption. Banks should take a holistic approach, focusing on people, processes, and technology, and prioritize data quality and governance. Building a skilled team, fostering a data-driven culture, and investing in technology and infrastructure are essential. Recommendation for Researchers: The integration of the TOE framework, the DOI theory, and the RBV theory can prove to be a powerful approach for comprehensively analyzing the various factors that influence BA adoption within the dynamic banking industry. Furthermore, this combined framework enables us to gain deeper insights into the subsequent impact of BA adoption on overall business performance. Impact on Society: Examining the factors influencing BA adoption in the banking industry and its subsequent impact on business performance can have wide-ranging societal implications. It can promote data-driven decision-making, enhance customer experiences, strengthen fraud detection, foster financial inclusion, contribute to economic growth, and trigger discussions on ethical considerations. Future Research: To further advance future research, there are several avenues to consider. One option is to broaden the scope by including a larger sample size, allowing for a more comprehensive analysis. Another possibility is to investigate the impact of BA adoption on various performance indicators beyond the ones already examined. Additionally, incorporating qualitative research methods would provide a more holistic understanding of the organizational dynamics and challenges associated with the adoption of BA in Jordanian commercial banks.