Data-driven Culture Research Articles

Evaluating the implementations of big data analytics in academic libraries: a structural equation model-based approach

PurposeThe rapid evolution of technological infrastructure and analytical capabilities has facilitated the integration of big data analytics (BDA) across various sectors. This study aims to investigate the suitability of implementing BDA within academic libraries, addressing the demanding need for effective data utilization in contemporary educational environments.Design/methodology/approachThe research is grounded in five critical components: data-driven culture, organizational infrastructure, employee responsibilities, management capabilities and the successful deployment of technology for BDA. An extensive literature review led to the development of a Likert scale-based questionnaire distributed on social media to collect data from university librarians in Pakistan. The authors were able to collect the data from 211 librarians. Descriptive statistics were employed to analyze the variables, while confirmatory factor analysis (CFA) was conducted using the AMOS to validate the research model.FindingsThe findings from the measurement model reveal significant positive correlations among all five components, underscoring their collective importance in facilitating the implementation of BDA. This formation is essential for addressing the evolving needs and academic requirements of users in the context of big data in a digital environment.Research limitations/implicationsThe study acknowledges limitations about its focus on a single country’s perspective, which may affect the generalizability of the findings regarding the implementation process of BDA in academic libraries.Originality/valueThis research contributes to the existing body of knowledge by highlighting the practices and capabilities of librarians in the era of big data as well as the requisite organizational infrastructure for the effective implementation of analytics in academic libraries.

Harnessing Business Analytics For Market Competitiveness: Discovering Pathways To Growth

Business analytics is becoming an essential tool for firms looking to boost market competitiveness and spur growth in a more competitive corporate environment. This study examines how analytics are essential for guiding strategic choices and enhancing operational effectiveness. The significance of the study stems from its capacity to provide light on how companies might use data to their advantage competitively. The main goals of this study are finding the critical elements that affect the effective application of business analytics, investigate how analytics support organizational development, and address the difficulties that businesses encounter in this pursuit. The study seeks to offer a thorough framework that combines theoretical viewpoints with real-world business analytics applications. The gap between the availability of advanced analytics technologies and how well firms use them is the main issue this study attempts to address. The collection includes pertinent case studies and opinions from analytics professionals and leaders in the field. The main conclusions show that to benefit from business analytics fully, real-time data use, technology improvements, and strategic alliances are essential. However, there are still major obstacles to adoption, such as large upfront investment costs and a lack of a data-driven culture. Focusing on particular businesses and possible biases in self-reported data are two of the weaknesses of the study. The theoretical ramifications point to the necessity of more research into frameworks that incorporate analytics into corporate strategy. The results provide useful suggestions for companies looking to improve their analytical skills and gain a competitive edge. Businesses can improve their position for long-term growth in a changing market by comprehending and addressing these trends.

Enterprise Data Governance : A Comprehensive Framework for Ensuring Data Integrity, Security, and Compliance in Modern Organizations

Enterprise data governance has emerged as a critical function for organizations seeking to maximize the value of their data assets while mitigating risks associated with data management. This article presents a comprehensive framework for implementing effective data governance strategies in modern enterprises. It examines the key components of robust governance structures, including clear policies, well-defined roles and responsibilities, and the establishment of data quality standards. The article explores methods for ensuring regulatory compliance and enhancing data security, emphasizing the importance of risk management and data protection measures. By analyzing case studies across various industries, the article identifies best practices and common challenges in data governance implementation. Additionally, it investigates the impact of emerging technologies such as artificial intelligence and blockchain on data governance practices. The findings underscore the necessity of a holistic approach to data governance that aligns with organizational objectives, fosters a data-driven culture, and adapts to evolving regulatory landscapes. This article contributes to the growing body of knowledge on enterprise data management and provides practical insights for organizations striving to improve their data governance capabilities in an increasingly complex digital environment.

Data analytics as a driver of digital transformation in financial institutions

Data analytics plays a pivotal role in driving digital transformation within financial institutions, enabling organizations to leverage insights from vast amounts of data to enhance decision-making and optimize operational performance. This paper explores the multifaceted impact of data analytics on the financial services sector, highlighting how it can improve customer experience, streamline processes, and foster innovation. By implementing advanced analytics techniques, financial institutions can better understand customer behaviour, identify market trends, and personalize services to meet evolving customer needs. The study discusses effective strategies for integrating data analytics into decision-making processes and operational workflows, emphasizing the importance of creating a data-driven culture that encourages collaboration across departments. Case studies of leading financial institutions that have successfully integrated data analytics into their operations illustrate the transformative outcomes, including improved customer satisfaction and increased revenue growth. Additionally, the paper addresses challenges such as data silos, regulatory compliance, and the need for skilled personnel to analyse and interpret data effectively. To overcome these hurdles, best practices are outlined, including investment in robust data infrastructure, fostering cross-functional teams, and ensuring continuous training for employees. The findings indicate that a strategic approach to data analytics not only enhances performance but also positions financial institutions to adapt to rapidly changing market conditions. Ultimately, this paper underscores the critical role of data analytics in shaping the future of financial services and achieving sustainable growth in an increasingly competitive landscape.

Artificial Intelligence in Strategic Business Decisions: Enhancing Market Competitiveness

Abstract: Integrating Artificial Intelligence (AI) into strategic decision-making is essential for enhancing market competitiveness in today's dynamic business environment. AI technologies such as machine learning, natural language processing (NLP), and predictive analytics optimize operations, personalize customer experiences, and drive product innovation. Machine learning algorithms analyze vast data to uncover patterns, aiding better decision-making. Predictive analytics forecasts market trends and consumer behaviors, allowing companies to anticipate demand and streamline supply chains, reducing risks like overproduction and stockouts. NLP-powered chatbots improve customer interactions by handling routine inquiries, freeing human agents for complex issues, and enabling personalized marketing. AI also accelerates product development by analyzing market data and consumer feedback, simulating scenarios, and predicting outcomes. Operational efficiency is enhanced through automation and optimized workflows, saving costs and increasing productivity. Despite these benefits, challenges such as data privacy, algorithmic bias, significant investment, and a shift to a data-driven culture must be managed. Effective AI integration offers significant competitive advantages, positioning companies to leverage predictive analytics, personalized customer interactions, and operational efficiency for growth and innovation.

Entrepreneurial potential in digital transformation: scale development using item response theory

PurposeThis study aims to develop a scale using item response theory (IRT) to assess the entrepreneurial potential for digital transformation in Brazilian companies.Design/methodology/approachIRT was used to develop a scale for entrepreneurial potential in digital transformation. This scale was constructed from a questionnaire, covering the domains: (1) data-driven culture, (2) openness to knowledge and adaptation to change, (3) connectivity and (4) creativity and innovation. The questionnaire was administered to a sample of 216 entrepreneurs from small business enterprise (SBE) and startups in Brazil.FindingsA questionnaire was developed and validated to assess the latent trait of entrepreneurial potential within the context of digital transformation. Additionally, a three-level scale of entrepreneurial potential was established: low (level I), intermediate (level II) and high (level III). The interpretation of this scale provides valuable information on which domains, such as data-driven culture, innovation, among others, can be enhanced to improve the potential of entrepreneur for digital transformation.Research limitations/implicationsThe sample was limited to small Brazilian companies and startups, which may restrict the applicability of the results to other business or geographic contexts. Additionally, the items evaluated in the scale may not fully capture all nuances of entrepreneurial potential for digital transformation. Future research should consider including new items that cover a broader range of entrepreneurial characteristics.Practical implicationsThe findings of this study have significant practical implications for the Brazilian entrepreneurial ecosystem, the entrepreneurs themselves, public policy makers and entrepreneurship support institutions. These results can guide digital transformation strategies, adjustments in public policies and investments, thereby promoting economic development and innovation in the country.Originality/valueThis study stands out for using IRT as a robust methodology to develop an interpretative scale to assess entrepreneurial potential in the digital transformation era. By focusing on Brazilian SBEs and startups, the study offers an original contribution on how these companies are handling the challenges of digitalization and identifying areas for improvement to further promote digital transformation among entrepreneurs.

Open Government Data Intention-Adoption Behavioural Model for Public Sector Organisations: A Technological Innovation Perspective

The objective of this research is to examine an open government data (OGD) intention-adoption behavioural model for the public sector organisations (PSOs), since examining the model is expected to lead to a better understanding of how to realise this technological innovation among PSOs on a large scale to excavate its innovative value. In this respect, we proposed a theoretical model to explore the factors that affect OGD adoption behaviour based on three dimensions of the TOE (technology, organisation, and environment) framework. The model was then analysed after collecting the survey data from 249 PSOs in Pakistan using a purposive sampling technique. The findings unfolded that the factors, except centralisation and civil society participation, framed in technology dimension (data resource, dataset quality, perceived benefits), organisation dimension (data-driven culture, digitisation capacity, need for transparency), and environment dimension (compliance pressure, political leadership commitment) affect the PSOs’ OGD adoption intention. Cumulatively, the intention to adopt OGD was found to have a significant positive impact on OGD adoption behaviour. Based on the TOE framework, the model, with the addition of adoption intention as a significant positive factor in adoption behaviour, bears a crucial theoretical and practical contribution in the context of OGD. Doi: 10.28991/ESJ-2024-08-05-04 Full Text: PDF

Integrating data analytics in academic institutions: enhancing research productivity and institutional efficiency

This review paper explores the strategic integration of data analytics within academic institutions, focusing on its potential to enhance research productivity and institutional efficiency. As higher education faces growing demands for improved outcomes and accountability, data analytics emerges as a critical tool for informed decision-making, resource optimization, and academic excellence. The paper begins by examining the role of data analytics in driving research insights, predictive planning, and collaboration, ultimately improving publication metrics and research impact. It then delves into how data analytics can streamline administrative processes, enhance student outcomes, and optimize resource management. The paper also addresses the significant challenges and barriers to integration, including concerns over data privacy, technical and organizational hurdles, and financial constraints. Finally, the paper offers recommendations for future directions, emphasizing strategic integration, capacity building, fostering a data-driven culture, and leveraging emerging technologies like AI and real-time data processing. These insights provide a comprehensive framework for academic institutions aiming to harness the power of data analytics to advance their research and operational goals.

BIG DATA-DRIVEN DECISION MAKING IN PROJECT MANAGEMENT: A COMPARATIVE ANALYSIS

This study investigates the impact of big data-driven decision-making in construction project management through a qualitative comparative analysis. By conducting semi-structured interviews with project managers, data analysts, and construction workers across various types of construction projects, the research identifies key themes related to the benefits and challenges of integrating big data analytics. The findings highlight significant advantages such as enhanced operational efficiency, improved decision-making processes, cost reduction, budget management, timely project delivery, and quality control and assurance. However, challenges including data integration complexities, privacy concerns, the need for specialized skills, and organizational resistance to change are also revealed. The study underscores the importance of fostering a data-driven culture and strong leadership support to maximize the benefits of big data in construction project management, while also emphasizing the need for context-specific strategies tailored to different project types.

Reading Instructional Leadership and Pedagogical Digitized Reading Practices in Selected Public Schools in Parañaque City

This research aims to correlate the assessment of the reading instructional leadership of the school administrators and extent of implementation of teachers on digitized reading Division of Parañaque, City. Drawing on survey data collected from a diverse sample of educators, the study assesses reading instructional leadership practices, implementation of digitized reading instruction, and the influence of demographic factors on educators' perceptions and practices. The findings reveal consistently high scores in reading instructional leadership, indicating a strong alignment between administrators' practices and the promotion of a shared vision for reading instruction. Additionally, teachers demonstrate a high level of implementation of digitized reading instruction, leveraging digital resources to enhance content delivery, teaching strategies, and assessment practices. While demographic factors such as age, civil status, length of service, and educational attainment do not significantly influence the implementation of digitized reading instruction, age emerges as a significant factor affecting assessment and overall implementation. The study underscores the importance of tailored support programs for educators, leadership development initiatives for administrators, and fostering a data-driven culture to inform instructional practices. Recommendations include promoting diversity and inclusivity, providing professional development opportunities, supporting effective leadership practices, fostering data-driven decision-making, and encouraging the integration of digital tools into reading instruction. By implementing these recommendations, educational stakeholders can create a supportive environment conducive to enhancing literacy outcomes and promoting student success.

Challenges and Issues in Implementing & Operationalising Big Data Analytics Capabilities in a major Australian Railway Organisation: A Case Study

This study examines the implementation of a Big Data Analytics (BDA) project within a major Australian freight and railway organisation. It also identifies the issues and challenges with data collection, data cleaning, data modelling, and data science software, and implements these models to deliver tangible business results. In addition, the project highlights the potential gains that a data analytics project, integrated with a data-driven culture, can provide through significant operational efficiencies and financial gains. Prior to 2019, the company had little exposure to Predictive Analytics. This study shows how the development of data science capability enables the creation of advanced predictive models, particularly in this case study, for the prediction of train wheel wear, and therefore a significant reduction in maintenance expenses Furthermore, a Data Analytics Maturity Assessment was conducted to determine the requirements to become a data-driven organisation. The outcome of the assessment was compared to recent global studies, and it was found that the organisation examined was significantly behind its counterparts in the areas of resources and analytic capabilities, and therefore required investment in these areas. Further studies to examine the degree of Data Analytics maturity within the Australian context are suggested. Organisations striving to become more data-driven need to plan and allocate resources for capability development in infrastructure, data management, employee quantitative skills, and governance.

Microfoundations of dynamic capabilities for digital transformation

PurposeScholars increasingly focus on digital transformation as a key strategy for incumbent firms to gain competitive advantages. Meeting digital transformation commitments presents challenges, requiring the application and the reconfiguration of dynamic capabilities. To address this need, this research proposes a framework of dynamic capabilities and its microfoundations to assess the opportunities and challenges regarding digital transformation, involving three dimensions: Digital sensing, digital seizing, and digital reconfiguring.Design/methodology/approachThis study employed a descriptive qualitative empirical approach, encompassing a sample of eight companies. Data triangulation was achieved through a combination of in-depth semi-structured interviews and secondary data.FindingsThe research provides evidence that data-driven culture fosters digital transformation and proposes the following new microfoundations: “Analytics for the customer experience journey” and “Digital analytics innovation management”, the internal barriers “Leadership without digital skills” and “Lack of strategic human resources management for digital transformation”, and the internal enablers “Defined strategy for digital transformation” and “Data-driven culture”. Finally, based on empirical results, it was possible to gather clues that link dynamic capabilities with digital maturity.Practical implicationsThe application of the proposed framework in companies enables them to develop a roadmap for the digital transition oriented towards their business and management strategies.Originality/valueThis paper contributes to the literature on dynamic capabilities for digital transformation by proposing new theoretical constructs that unearth their microfoundations, barriers, and enablers.

Data-driven strategies for business expansion: Utilizing predictive analytics for enhanced profitability and opportunity identification

In today's hyper-competitive business landscape, leveraging data-driven strategies is paramount for sustainable growth and profitability. This review presents an overview of the imperative role of predictive analytics in facilitating business expansion by enhancing profitability and identifying opportunities. Predictive analytics harnesses historical data and advanced modeling techniques to forecast future trends, enabling businesses to make informed decisions with precision. By understanding predictive analytics, businesses can effectively identify expansion opportunities by analyzing market trends, segmenting customer bases, and uncovering new markets and niches. Moreover, predictive analytics empowers organizations to enhance profitability through optimized pricing strategies, demand forecasting, and personalized marketing initiatives tailored to customer preferences and behaviors. Implementing data-driven strategies for business expansion requires building a robust data infrastructure, selecting appropriate tools and technologies, and fostering a data-driven culture within the organization. Real-world case studies illustrate the transformative impact of predictive analytics on business expansion, offering insights into best practices and lessons learned. Despite the undeniable benefits of predictive analytics, challenges such as data privacy concerns, ensuring data quality, and organizational resistance must be addressed. However, the rewards of embracing data-driven decision-making far outweigh the challenges, positioning businesses for sustainable growth and competitive advantage in the dynamic marketplace. In conclusion, this review emphasizes the critical role of predictive analytics in driving business expansion by enhancing profitability and opportunity identification. By harnessing the power of data, businesses can unlock new avenues for growth, mitigate risks, and stay ahead of the curve in today's data-driven economy.

Big Data Analytics Adoption in Manufacturing Companies: The Contingent Role of Data-Driven Culture

Big Data Analytics Adoption in Manufacturing Companies: The Contingent Role of Data-Driven Culture

Marketing Agility’s Influence on Market Performance: A Perspective from FinTech Industry

In the dynamic market of Pakistan, the FinTech industry faces many challenges in grabbing the market share. FinTech startups must develop the dynamic capabilities to cope with these challenges and enhance their market performance. Marketing agility involves different dynamic capabilities that can improve organizational operations. Therefore, this research aimed to investigate the influence of marketing agility on the market performance of FinTechs. Moreover, it investigated the moderating role of data-driven culture in the relationship between marketing agility and market performance. The data for the research were gathered from 447 managers of FinTech companies via a close-ended questionnaire. The preliminary analysis reported seven outliers, which were removed during screening. Thus, 440 responses were retained for final analysis. The demographic characteristics of managers were determined using SPSS. In addition, Smart PLS-4 was used for data analysis. It analyzed the data in two steps; in the first step, the measurement model was accessed, and the second step investigated the structural model to test the hypotheses. The results highlighted the significant relationship between marketing agility and performance. In addition, the findings confirmed the moderation of data-driven culture in the relationship between marketing agility and market performance. The research results can act as a guide for managers of Fintech startups aiming to enhance their market performance in the volatile market of Pakistan.

Developing customer analytics capability in firms of different ages: Examining the complementarity of outside-in and inside-out resources

Customer analytics capability remains underdeveloped among firms despite its potential for enhancing competitiveness. Previous research has predominantly focused on inside-out organizational factors as drivers of customer analytics capability. This paper examines the role of outside-in resource, the complementarity between outside-in and inside-out resources, and their boundary conditions. Specifically, we study how customer orientation culture (an outside-in resource) complements data-driven culture (an inside-out resource) in firms of different ages to drive customer analytics capability and subsequently, firm performance. Using survey data obtained from Canadian firms, we find that customer orientation is not only positively related to customer analytics capability but also reinforces the effect of data-driven culture. We further find that the conditional effect of customer orientation becomes stronger as firm age increases. In particular, among older firms, the impact of data-driven culture is greatest when customer orientation is high, but it becomes nonsignificant when customer orientation is low. We also link these relationships to firm performance using mediation and moderated mediation analyses. Overall, the results suggest that achieving customer analytics excellence and resultant competitive performance requires marketing to continuously act as customer champions and advocate data analytics efforts to ensure the firm embraces an outside-in orientation.

Corporate social responsibility: an enabler of organizational resilience

PurposeDrawing on social capital theory, this study aims to explore the effect of corporate social responsibility (CSR) on organizational resilience. The research investigates the mediating role of relationship quality in the association of CSR with organizational resilience, and the moderating role of data-driven culture in the association between CSR and relationship quality.Design/methodology/approachData were collected from Chinese agricultural firms with a sample of 241 senior or middle executives and structural equation modeling was used to test the research model and hypotheses.FindingsThe results indicate that CSR positively affects the relationship quality between agribusinesses and farmers, which in turn positively affects both proactive resilience and reactive resilience. Relationship quality has a partial mediating role in the association of CSR with proactive resilience and reactive resilience. Data-driven culture has a positive moderating effect on the relationship between CSR and relationship quality.Originality/valueBy arguing for CSR toward organizational resilience and analyzing its underlying mechanism, this study enriches the literature on CSR and organizational resilience and expands the existing knowledge on the roles of relationship quality and data-driven culture. This study also provides practical insights into how to improve organizational resilience.

The mediating effect of data literacy competence in the relationship between data governance and data-driven culture

PurposeThis research investigates the critical role of data governance (DG) in shaping a data-driven culture (DDC) within organizations, recognizing the transformative potential of data utilization for efficiency, opportunities, and productivity. The study delves into the influence of DG on DDC, emphasizing the mediating effect of data literacy (DL).Design/methodology/approachThe study empirically assesses 125 experienced managers in Indonesian public service sector organizations using a quantitative approach. Structural Equation Modeling (SEM) analysis was chosen to examine the impact of DG on DDC and the mediating effects of DL on this relationship.FindingsThe findings highlight that both DG and DL serve as antecedents to DDC, with DL identified as a crucial mediator, explaining a significant portion of the effects between DG and DDC.Research limitations/implicationsBeyond unveiling these relationships, the study discusses practical implications for organizational leaders and managers, emphasizing the need for effective policies and strategies in data-driven decision-making.Originality/valueThis research fills an important research gap by introducing an original model and providing empirical evidence on the dynamic interplay between DG, DL, and DDC, contributing to the evolving landscape of data-driven organizational cultures.

A REVIEW OF DATA-DRIVEN DECISION MAKING IN ENGINEERING MANAGEMENT

This research review article explores the transformative impact of data-driven decision-making (DDDM) across various sectors, highlighting the integration of advanced analytics to enhance organizational efficiency, innovation, and strategic planning. Despite the potential benefits, the adoption of DDDM poses significant challenges, including data quality issues, integration complexities, and the need for a cultural shift towards valuing data analytics. Through a comprehensive analysis of recent research and case studies, this article synthesizes key findings, emerging trends, and future research areas in DDDM. It provides practical recommendations for practitioners aiming to implement and optimize DDDM processes, emphasizing the importance of fostering a data-driven culture, investing in robust data infrastructure, and ensuring the ethical use of data. Additionally, the article offers suggestions for continuous improvement and adaptation to technological advancements, advocating for regular strategy reviews, monitoring emerging trends, and fostering innovation. By addressing these challenges and leveraging the outlined recommendations, organizations can unlock the full potential of DDDM, driving significant advancements in efficiency, competitiveness, and strategic decision-making in the digital age.
 Keywords: Data-driven Decision Making, Engineering Management, Machine Learning, Big Data, Advanced Analytics, Organizational Efficiency, Data Quality and Infrastructure, Technological Advancements.

The impact of big data analytics on innovation capability and sustainability performance of hotels: evidence from an emerging economy

PurposeThis study aimed to examine the role of big data analytics capabilities (BDAC) in fostering organizational innovation capabilities and, consequently, in achieving economic, social and environmental sustainability.Design/methodology/approachThrough the lens of dynamic capability theory, this study surveyed 115 hotels using purposive sampling to gain in-depth insights regarding the factors affecting organizational sustainability in the hospitality industry. The data analysis was conducted using partial least squares-structural equation modeling (PLS-SEM).FindingsThe findings reported a substantial impact of seven core dimensions (i.e. technology, data, basic resources, technological skills, managerial skills, organizational learning and data-driven culture) in building BDAC among hotels. Moreover, BDAC was also revealed to significantly influence innovation capabilities, positively impacting all three sorts of sustainability performance. Innovation capability also mediated the relationship between BDAC and all sustainability factors.Practical implicationsThe findings will assist policymakers and practitioners in developing effective initiatives to enhance the adoption and implementation of data science and technologies, substantially contributing to the “National IR 4.0 Policy” and “Malaysia Digital Economy Blueprint” and achieving sustainable development goals (SDGs).Originality/valueThe originality of this study is established by investigating the interplay between BDAC, innovation capability and sustainability performance, particularly in the context of the hotel industry, whereas the existing studies focus on exploring the advantages of BDA.