Big data analytics adoption: Determinants and performances among small to medium-sized enterprises

Blockchain technology adoption in healthcare: an integrated model

Blockchain technology has gained significant attention in several sectors owing to its distributed ledger, decentralized nature, and cryptographic security. Despite its potential to reform the healthcare industry by providing a unified and secure system for health records, blockchain adoption remains limited. This study aimed to identify the factors influencing the intention to adopt blockchain in healthcare by focusing on healthcare providers. A theoretical model is proposed by integrating the Technological-Organizational-Environmental framework, Fit-Viability Model, and institutional theory. A quantitative approach was adopted and data were collected through an online survey of 199 hospitals to evaluate the model. The collected data were analysed using PLS-SEM. The results indicated that technology trust, information transparency, disintermediation, cost-effectiveness, top management support, organizational readiness, partner readiness, technology vendor support, fit, and viability significantly and positively influenced the intention to adopt blockchain-based Health Information Systems in hospitals. Conversely, coercive pressure from the government negatively affects adoption decisions. Moreover, the study found that the hospital ownership type did not moderate the relationship between the identified factors and blockchain adoption. This study provides valuable insights into the various factors that influence blockchain adoption in hospitals. The developed model offers guidelines for hospitals, blockchain providers, governments, and policymakers to devise strategies that promote implementation and encourage widespread adoption of blockchain in healthcare organizations.

Readiness of big health data analytics by technology-organization-environment (TOE) framework in Ethiopian health sectors

Background

Big health data is a large and complex dataset that the health sector has collected and stored continuously to generate healthcare evidence for intervening the future healthcare uncertainty. However, data use for decision-making practices has been significantly low in developing countries, especially in Ethiopia. Hence, it is critical to ascertain which elements influence the health sector's decision to adopt big health data analytics in health sectors. The aim of this study was to identify the level of readiness for big health data analytics and its associated factors in healthcare sectors.

Methods

A cross-sectional study design was conducted among 845 target employees using the structural equation modeling approach by using technological, organizational, and environmental (TOE) frameworks. The target population of the study was health sector managers, directors, team leaders, healthcare planning officers, ICT/IT managers, and health professionals. For data analysis, exploratory factor analysis using SPSS 20.0 and structural equation modeling using AMOS software were used.

Result

58.85 % of the study participants had big health data analytics readiness. Complexity (CX), Top management support (TMS), training (TR) and government law policies and legislation (GLAL) and government IT policies (GITP) had positive direct effect, compatibility (CT), and optimism (OP) had negative direct effect on BD readiness (BDR).

Conclusion

The technological, organizational, and environmental factors significantly contributed to big health data readiness in the healthcare sector. The Complexity, compatibility, optimism, Top management support, training (TR) and government law and IT policies (GITP) had effect on big health data analytics readiness. Formulating efficient reform in healthcare sectors, especially for evidence-based decision-making and jointly working with stakeholders will be more relevant for effective implementation of big health data analytics in healthcare sectors.

Factors impacting the adoption of big data in healthcare: A systematic literature review

BACKGROUND

The term "big data" refers to the vast volume, variety, and velocity of data generated from various sources-e.g., sensors, social media, and online platforms. Big data adoption within healthcare poses an intriguing possibility for improving patients' health, increasing operational efficiency, and enabling data-driven decision-making. Despite considerable interest in the adoption of big data in healthcare, empirical research assessing the factors impacting the adoption process is lacking. Therefore, this review aimed to investigate the literature using a systematic approach to explore the factors that affect big data adoption in healthcare.

METHODS

A systematic literature review was conducted. The methodical and thorough process of discovering, assessing, and synthesizing relevant studies provided a full review of the available data. Several databases were used for the information search. Most of the articles retrieved from the search came from popular medical research databases, such as Scopus, Taylor & Francis, ScienceDirect, Emerald Insights, PubMed, Springer, IEEE, MDPI, Google Scholar, ProQuest Central, ProQuest Public Health Database, and MEDLINE.

RESULTS AND CONCLUSION

The results of the systematic literature review indicated that several theoretical frameworks (including the technology acceptance model; the technology, organization, and environment framework; the interactive communication technology adoption model; diffusion of innovation theory; dynamic capabilities theory; and the absorptive capability framework) can be used to analyze and understand technology acceptance in healthcare. It is vital to consider the safety of electronic health records during the use of big data. Furthermore, several elements were found to determine technological acceptance, including environmental, technological, organizational, political, and regulatory factors.

Development of big data assisted effective enterprise resource planning framework for smart human resource management

The planning of human resources and the management of enterprises consider the organization's size, the amount of effort put into operations, and the level of productivity. Inefficient allocation of resources in organizations due to skill-task misalignment lowers production and operational efficiency. This study addresses organizations' poor resource allocation and use, which reduces productivity and the efficiency of operations, and inefficiency may adversely impact company production and finances. This research aims to develop and assess a Placement-Assisted Resource Management Scheme (PRMS) to improve resource allocation and usage and businesses' operational efficiency and productivity. PRMS uses expertise, business requirements, and processes that are driven by data to match resources with activities that align with their capabilities and require them to perform promptly. The proposed system PRMS outperforms existing approaches on various performance metrics at two distinct levels of operations and operating levels, with a success rate of 0.9328% and 0.9302%, minimal swapping ratios of 12.052% and 11.658%, smaller resource mitigation ratios of 4.098% and 4.815%, mean decision times of 5.414s and 4.976s, and data analysis counts of 6387 and 6335 Success and data analysis increase by 9.98% and 8.2%, respectively, with the proposed strategy. This technique cuts the switching ratio, resource mitigation, and decision time by 6.52%, 13.84%, and 8.49%. The study concluded that PRMS is a solid, productivity-focused corporate improvement method that optimizes the allocation of resources and meets business needs.

Information technology capability, open technological innovation and firm growth

The aim of this paper is to investigate the effects of information technology (IT) capability on firm growth in the context of open technological innovation. The paper utilized a logical deductive approach to develop hypotheses and analytical frameworks, and collected empirical data from 256 Chinese new ventures. Regression analysis and structural equation models were used to test the hypotheses and analyze the data. The results showed that IT capability, including flexibility and integration of information technology, significantly influenced firm growth, and open technological innovation partially mediated the relationship between IT flexibility and firm growth, and significantly mediated the relationship between IT integration and firm growth. The paper's limitations include the cross-sectional design, limited sample size, and potential unobserved variables such as organizational learning that could affect the relationship between IT capability and firm growth. The research is the first to investigate the effects of IT capability on firm growth based on the mediation of open technological innovation in China, contributing to the literature on IT capability and providing insights for managerial practice in the sharing economy era.

Analysing the current status, hotspots, and future trends of technology management: Using the WoS and scopus database

This study presents a comprehensive overview of the development process and the latest trends in technology management (TM), laying a robust foundation for further advancements in this domain. To achieve this, we analysed 1944 TM articles from the Web of Science database and 2642 articles from Scopus, spanning the last 20 years. Employing methodologies that involve scientific knowledge graphs and bibliometrics, we analysed diverse aspects such as changes in the annual publication of articles; geographical distribution among countries, institutions, disciplines, and authors; keyword co-occurrence and clusters; and timezone view. Our findings reveal a significant surge in TM's growth in recent years, showcasing its highly promising potential. The USA is the frontrunner in contributing to TM research, followed by China and the UK. TM research is relatively concentrated in the UK, while it appears more dispersed in China. The University of Cambridge had the highest volume of research, and the disciplines of Business, Management, Engineering, and Computer Science occupied the top spots. As TM evolves, a possible challenge could be the emergence of new authoritative authors. Second, TM's vibrant landscape is characterised by hotspots such as innovation, technology strategy, technology acquisition, technology application, technology standards, and sustainable development. Among these, information and medical technologies stand out as the most frequently referenced technologies. Third, the trends in TM are as follows: innovation is subdivided into technological innovation and open innovation, bibliometric analysis and patent analysis have become pivotal methods for knowledge management, the scope of TM has expanded from internal organisational processes to encompass external aspects, and TM is gradually evolving into a mature science, with its focus transitioning from macro to micro and becoming more profound and detailed. Last, Industry 4.0, artificial intelligence, big data, and the IoT represent the latest frontier technologies in the realm of TM.

Determinants of cloud computing integration and its impact on sustainable performance in SMEs: An empirical investigation using the SEM-ANN approach

Although extant literature has thoroughly investigated the incorporation of cloud computing services, examining their influence on sustainable performance, particularly at the organizational level, is insufficient. Consequently, the present research aims to assess the factors that impact the integration of cloud computing within small and medium-sized enterprises (SMEs) and its subsequent effects on environmental, financial, and social performance. The data were collected from 415 SMEs and were analyzed using a hybrid SEM-ANN approach. PLS-SEM results indicate that relative advantage, complexity, compatibility, top management support, cost reduction, and government support significantly affect cloud computing integration. This study also empirically demonstrated that SMEs could improve their financial, environmental, and social performance by integrating cloud computing services. ANN results show that complexity, with a normalized importance (NI) of 89.14%, is ranked the first among other factors affecting cloud computing integration in SMEs. This is followed by cost reduction (NI = 82.67%), government support (NI = 73.37%), compatibility (NI = 70.02%), top management support (NI = 52.43%), and relative advantage (NI = 48.72%). Theoretically, this study goes beyond examining the determinants affecting cloud computing integration by examining their impact on SMEs' environmental, financial, and social performance in a comprehensive manner. The study also provides several practical implications for policymakers, SME managers, and cloud computing service providers.

Factors for the implementation of the circular economy in Big Data environments in service companies in post pandemic times of COVID-19: The case of Colombia

In emerging economies, Big Data (BD) analytics has become increasingly popular, particularly regarding the opportunities and expected benefits. Such analyzes have identified that the production and consumption of goods and services, while unavoidable, have proven to be unsustainable and inefficient. For this reason, the concept of the circular economy (CE) has emerged strongly as a sustainable approach that contributes to the eco-efficient use of resources. However, to develop a circular economy in DB environments, it is necessary to understand what factors influence the intention to accept its implementation. The main objective of this research was to assess the influence of attitudes, subjective norms, and perceived behavioral norms on the intention to adopt CE in BD-mediated environments. The methodology is quantitative, cross-sectional with a descriptive correlational approach, based on the theory of planned behavior and a Partial Least Squares Structural Equation Model (PLS-SEM). A total of 413 Colombian service SMEs participated in the study. The results show that managers' attitudes, subjective norms, and perceived norms of behavior positively influence the intentions of organizations to implement CB best practices. Furthermore, most organizations have positive intentions toward CE and that these intentions positively influence the adoption of DB; however, the lack of government support and cultural barriers are perceived as the main limitation for its adoption. The research leads to the conclusion that BD helps business and government develop strategies to move toward CE, and that there is a clear positive will and intent toward a more restorative and sustainable corporate strategy.

Identifying barriers to big data analytics adoption in circular agri-food supply chains: a case study in Turkey

Big data analytics (BDA), along with the resource efficiency and sustainability perspectives of a circular economy, supports the transition to circular agri-food supply chains (AFSCs), contributing to a country's achievement of the United Nations' Sustainable Development Goals. However, there is still limited research demonstrating the importance and awareness of BDA implementation in circular AFSCs in developing countries. As a result of the barriers to BDA adoption in these regions, circular AFSCs in developing countries are still in their infancies. This study sought to identify the barriers to BDA adoption in circular AFSCs in Turkey using a Delphi-based Pythagorean fuzzy analytic hierarchy process. The proposed method removes the potential for bias and produces consensus among managers of companies in various AFSCs in Turkey. The findings of this study show that the most impactful barriers to BDA are technical, economic and social, followed by environmental and organisational. The most crucial sub-barriers to BDA adoption are "lack of trust, privacy and security", "lack of financial resources" and "lack of skilled human resources". This research can guide industry managers and policymakers in the development of strategies for overcoming barriers to BDA adoption in circular AFSCs in developing nations.

Leagile manufacturing system adoption in an emerging economy: an examination of technological, organizational and environmental drivers

Purpose

Rapid changes in the global environment and the effects of existing economic issues triggered by COVID-19 and the war in Ukraine have posed several challenges for manufacturing firms. A hybrid strategy integrating lean and agile (leagile) systems is viable for firms to enhance their capabilities in such dynamic contexts. This paper examines the critical drivers of leagile manufacturing system adoption in an emerging economy from the technological, organizational and environmental (TOE) perspective.

Design/methodology/approach

A cross-sectional survey is carried out to obtain data from 438 managers working in 219 manufacturing firms. Multiple regression analysis is applied to test the effect of technological, organizational and environmental drivers on the adoption of leagile systems.

Findings

The results show that organization capacity, environmental uncertainty and relative advantage demonstrate the most significant positive relationships with the leagile systems adoption wherein complexity and resistance to change appear to exhibit significant negative associations. Unexpectedly, firm size unveils no significant effect on the adoption of leagile systems.

Practical implications

To deal effectively with critical challenges triggered by ever-changing environment, firms have sought to adopt innovative systems for achieving products' availability in the markets at the right quality and price. A hybrid strategy integrating lean and agile (leagile) systems is viable to enhance a firm's capabilities in such dynamic contexts. The findings of our study help top management and policymakers identify and assess the critical drivers that may facilitate or hinder the successful adoption of leagile systems.

Originality/value

A major trend of studies in the field of manufacturing systems has focused on the critical success factors of adopting either lean or agile systems. Furthermore, research work concerning leagile as a hybrid system focuses primarily on the conceptual development rather than empirical grounds of leagile systems. Given the lack of empirical research in this field, this study offers an early attempt to predict leagile system adoption in an emerging economy. It also contributes to the manufacturing systems research by extending the extant knowledge about the role of firm-level drivers in leagile system adoption from the TOE perspective.

Analysis of the Conditions Influencing the Assimilation of the Robotic Process Automation by Enterprises

More and more companies are implementing the RPA (Robotic Process Automation) tools that belong to the newly emerging category of IT solutions used to automate business processes and enable the development of the so-called software robots. The term robot has a metaphorical meaning here – it is a special kind of software, not a device. Due to the fact that this is a new product category and many companies do not have extensive experience in this area yet, the use of the RPA tools is associated with many risks. At the same time, due to the increasingly competitive environment, it seems that there is no turning back from the implementation thereof. For this reason, two goals have been set in the article. The first is to build and verify a research model based on the TOE model (Technology-Organization-Environment), allowing for the identification of the determinants (drivers) influencing the assimilation of the robotic process automation by enterprises. The second goal is to develop recommendations for the managers responsible for implementing the RPA tools that will allow for increasing the assimilation of the robotic process automation. The following methods were used to accomplish these goals: literature research, a survey (conducted on 267 Polish enterprises) and statistical analysis (with the use of the structural equation models).

Digital transformation, smart technologies, and eco-innovation are paving the way toward sustainable supply chain performance

The world has limited resources and resource depletion has been a serious concern for many years. To ensure that ecological balance is maintained, the United Nations has developed 17 Sustainable Development Goals (SDGs). This study attempts to meet the 12th SDG of "responsible production and consumption" and provide a guideline to manufacturing organizations in Pakistan. Many scholars have examined the role of different technologies on sustainable performance. However, research using eco-innovation (EI), digital transformation (DT), and smart technologies (ST) is still scarce. The current study develops the conceptual model based on the resource-based view (RBV) and technology, organization, and environment (TOE) theories, and using a simple random sampling technique, valid data from 375 professionals in the manufacturing industry is collected to test the relationship between sustainable development strategy (SDS), EI, DT, ST, and sustainable supply chain performance (SSCP). The results indicate strong positive relationships between SDS, EI, DT, ST, and SSCP. Results indicate that even if organizations are executing SDS and EI, without DT and ST, organizations cannot achieve SSCP. Therefore, it is recommended to the managers that they must apply DT and ST to guarantee their pursuit of achieving sustainable performance and contribution toward SDGs.

The influencing mechanism of big data analytics technology capability on enterprise's operational performance: The mediating role of data-tool fit

With the development of network technology, enterprises face the explosive growth of data every day. Therefore, to fully mine the value of massive data, big data analysis (BDA) technology has become the key to developing the core competitiveness of enterprises. However, few empirical studies have investigated the influencing mechanism of the BDA capability of an enterprise on its operational performance. To fill this gap, this study explores how BDA technology capability influences enterprise operation performance, based on dynamic capabilities theory and resource-based theory. It proposes the key variables, including the connectivity, compatibility, and modularization of big data analysis technical capability, enterprise's operational performance, and the fit between data and tools, to establish a model and study the correlation between the variables. The results highlight the mediating role of data-tool fit in the relationships between BDA capability and the enterprise's operational performance, which is a major finding that has not been underlined in the extant literature. This study provides valuable insight for operational managers to help them in mobilizing BDA capability for enterprises' operational management and improving operational performance.

Diffusion of Big Data Analytics Innovation in Managing Natural Resources in the African Mining Industry

The study draws upon the ethical theory of organizing to elucidate the links between ethical climate antecedents, organizational practices, and consequences. We also integrated organizing vision theory to examine the influence of diffusion of big data analytics innovation on sustainable business practices. The results indicate that organizational trust has a significant positive impact on ethics training and ethics audit, which is critical to South Africa's mining industry. Furthermore, the results indicate that ethical leadership is positively related to ethics training and ethics audits. Findings show that ethics training and ethics audit is positively related to sustainable business practices. Findings indicate that the vision constructed by community members is positively related to the diffusion of big data analytics innovation. We also found that the diffusion of big data analytics innovation is positively related to sustainable business practices. Lastly, findings show that sustainable business practices are positively related to firm performance.

Informatization of Accounting Systems in Small- and Medium-Sized Enterprises Based on Artificial Intelligence-Enabled Cloud Computing

Against the backdrop of China's growing market economy, small- and medium-sized enterprises (SMEs) have taken advantage of this opportunity to develop rapidly. At present, SMEs have become an important part of the market economy. Accounting system information management system is an advanced form of management, and improving the degree of accounting information is the key to improving the management mode of SMEs. This study applies cloud computing to enterprise accounting management systems. The results show that realizing SME accounting information management can effectively improve economic settlements. With the development of cloud computing, its improvement of accounting management efficiency cannot be ignored. Besides, the risks of accounting informatization, enterprises can make their development by establishing a secure network protection wall and relying on strict relevant laws and regulations.

An application of data mining algorithms for predicting factors affecting Big Data Analysis adoption readiness in SMEs

The adoption of Big Data Analysis (BDA) has become popular among firms since it creates evidence for decision-making by managers. However, the adoption of BDA continues to be poor among small and medium enterprises (SMEs). Therefore, this study adopted the Technology-Organization-Environment (TOE) framework to identify the drivers of readiness to adopt BDA among SMEs. Chi-square automatic interaction detection (CHAID), Bayesian network, neural network, and C5.0 algorithms of data mining were utilized to analyze data collected from 240 Vietnamese managers of SMEs. The evaluation model identified the C5.0 algorithm as the best model, with accurate results for the prediction of factors influencing the readiness to adopt BDA among SMEs. The findings revealed management support, data quality, firm size, data security and cost to be the fundamental factors influencing BDA adoption readiness. Moreover, the results identified the service sector as having a higher level of readiness toward the adoption of BDA compared to the manufacturing sector. The findings are imperative for the enhancement of the decision-making process and advancement of comprehension of the determinants of BDA adoption among SMEs by researchers, managers, providers and policymakers.

Examining the critical factors of computer-assisted audit tools and techniques adoption in the post-COVID-19 period: internal auditors perspective

Purpose

In light of the repercussions of the COVID-19 pandemic, electronic auditing otherwise known as computer-assisted audit tools and techniques (CAATTs) has become inevitable to automate the auditing process worldwide. Accordingly, the purpose of this study is to examine the influence of technological, organizational and environmental (TOE) factors on public sector adoption of CAATTs in developing countries such as Jordan under the COVID-19 pandemic conditions.

Design/methodology/approach

This study used 136 usable responses from the managers of internal audit (IA) of the Jordanian public sector entities. The data collected were analyzed using partial least squares-structural equation modeling. The TOE framework has been used in this study to consider a wide set of TOE factors. Then, this study suggests a CAATTs adoption model that incorporates the related technology factors of the diffusion of innovation theory to environmental and organizational factors. Further, this study contributes to the TOE framework by addressing government regulations, audit bodies’ support and audit task complexity as environmental factors affecting CAATTs adoption in the context of the public sector.

Findings

The results revealed that for technological factors, only the compatibility affects CAATTs adoption by the IA departments. For organizational factors, organizational readiness, top management support, auditors’ information technology competency and entity size were found to be significant factors. From the environmental factors, both government regulation and audit task complexity influence the CAATTs adoption. Besides, entity size moderates the influence of top management support on the CAATTs adoption in the public sector.

Practical implications

The findings could highlight the significance of the CAATTs adoption in the public sector institutions (by internal auditors) post-COVID-19, taking into consideration the TOE framework’s factors. Also, the findings are significant for the decision-makers and regulators in declaring new legislation for the electronic IA profession in the Jordanian public sector.

Social implications

It turns out that the CAATTs adoption in the public sector can definitely enhance their ability to achieve the role of IA in preserving public funds and restricting corrupt practices within the public sector.

Originality/value

To the best of the authors’ knowledge, this study is one of the first studies that address the professional audit agency support and audit task complexity as environmental factors, as well as the entity size as an organizational factor, that affect CAATTs adoption in the IA department of the public sector.

A novel approach for DDoS attacks detection in COVID-19 scenario for small entrepreneurs

The current COVID-19 issue has altered the way of doing business. Now that most customers prefer to do business online, many companies are shifting their business models, which attracts cyber attackers to launch several kinds of cyberattacks against commercial companies simultaneously. The most common and lethal DDoS attack disables the victim's online resources. While large businesses can afford defensive measures against DDoS assaults, the situation is different for new entrepreneurs. Their lack of security resources restricts their ability to ward off DDoS attacks. Here, we aim to highlight the problems that prospective entrepreneurs should be aware of before joining the business, followed by a filtering mechanism that efficiently identifies DDoS assaults in the COVID-19 scenario, which is the subject of our research. The suggested approach employs statistical and machine learning techniques to discriminate between DDoS attack data and regular communication. Our suggested framework is cost-effective and identifies DDoS attack traffic with a 92.8% accuracy rate.

A novel approach for DDoS attacks detection in COVID-19 scenario for small entrepreneurs

The current COVID-19 issue has altered the way of doing business. Now that most customers prefer to do business online, many companies are shifting their business models, which attracts cyber attackers to launch several kinds of cyberattacks against commercial companies simultaneously. The most common and lethal DDoS attack disables the victim's online resources. While large businesses can afford defensive measures against DDoS assaults, the situation is different for new entrepreneurs. Their lack of security resources restricts their ability to ward off DDoS attacks. Here, we aim to highlight the problems that prospective entrepreneurs should be aware of before joining the business, followed by a filtering mechanism that efficiently identifies DDoS assaults in the COVID-19 scenario, which is the subject of our research. The suggested approach employs statistical and machine learning techniques to discriminate between DDoS attack data and regular communication. Our suggested framework is cost-effective and identifies DDoS attack traffic with a 92.8% accuracy rate.

Digital Technology Adoption and Sustainable Development Performance of Strategic Emerging Industries

Although digital technology adoption has received more attention from researchers in the field of innovation management research, the micro mechanism of the impact of digital technology adoption on the sustainable development of enterprises has not been fully investigated. The objective of this study is to identify the existing relationships between digital technology adoption, digital technology capability, digital strategy, and sustainable development performance of strategic emerging industries. A theoretical conceptual model was developed that analyzed the primary data from 385 sample enterprises in strategic emerging industries. The results indicated that digital technology adoption had a positive influence on both enterprises' economic performance and environmental performance in strategic emerging industries. Digital technology capability played a mediating role in the relationship between digital technology adoption and enterprises' economic performance and environmental performance. And digital strategy strengthened the influence of digital technology adoption on enterprises.

Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMEs

Big data (BD) analytics has been increasingly gaining attraction in both practice and theory in light of its opportunities, barriers and expected benefits. In particular, emerging economics view big data analytics as having great importance despite the fact that it has been in a constant struggle with the barriers that prevent its adoption. Thus, this study primarily attempted to determine the drivers of big data analytics in the context of a developing economy, Jordan. The study examined the influence of technological, organizational and environmental factors on big data adoption in the Jordanian SMEs context, using PLS-SEM for the analysis. The empirical results revealed that the relative advantage, complexity, security, top management support, organizational readiness and government support influence the adoption of BD, whilst pressure of competition and compatibility appeared to be of insignificant influence. The findings are expected to contribute to enterprise management and strategic use of data analytics in the present dynamic market environment, for both researcher and practitioner circles concerned with the adoption of big data in developing countries.

Business Intelligence and Business Value in Organisations: A Systematic Literature Review

Organisations must derive adequate business value (BV) from Business Intelligence (BI) adoption to retain their profitability and long-term sustainability. Yet, the nuances that define the realisation of BV from BI are still not understood by many organisations that have adopted BI. This paper aims to foster a deeper understanding of the relationship between Business Intelligence (BI) and business value (BV) by focusing on the theories that have been used, the critical factors of BV derivation, the inhibitors of BV, and the different forms of BV. To do this, a systematic literature review (SLR) methodology was adopted. Articles were retrieved from three scholarly databases, namely Google Scholar, Scopus, and Science Direct, based on relevant search strings. Inclusion and exclusion criteria were applied to select ninety-three (93) papers as the primary studies. We found that the most used theoretical frameworks in studies on BI and BV are the Resource-Based View (RBV), Dynamic Capabilities Theory (DCT), Technology-Organisation-Environment (TOE), and Contingency Theory (CON). The most acknowledged critical factors of BV are skilled human capital, BI Infrastructure, data quality, BI application and usage/data culture, BI alignment with organisational goals, and top management support. The most acclaimed inhibitors of BV are data quality and handling, data security and protection, lack of BI Infrastructure, and lack of skilled human resource capital, while customer intelligence is the most acknowledged form of BV. So far, many theories that are relevant to BI and BV, critical factors, inhibitors, and forms of BV were marginally mentioned in the literature, requiring more investigations. The study reveals opportunities for future research that can be explored to gain a deeper understanding of the issues of BV derivation from BI. It also offers useful insights for adopters of BI, BI researchers, and BI practitioners.

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

Purpose

The study presents various barriers to adopt big data analytics (BDA) for sustainable manufacturing operations (SMOs) post-coronavirus disease (COVID-19) pandemics. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the graph theory matrix approach (GTMA) is applied.

Design/methodology/approach

The study presents various barriers to adopt BDA for the SMOs post-COVID-19 pandemic. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the GTMA is applied.

Findings

The study identifies barriers to investment in BDA implementation. It categorizes the barriers based on factor analysis and computes the intensity for each category of a barrier for BDA investment for SMOs. It is observed that the organizational barriers have the highest intensity whereas the human barriers have the smallest intensity.

Practical implications

This study may help organizations to take strategic decisions for investing in BDA applications for achieving one of the sustainable development goals. Organizations should prioritize their efforts first to counter the barriers under the category of organizational barriers followed by barriers in data management and human barriers.

Originality/value

The novelty of this paper is that barriers to BDA investment for SMOs in the context of Indian manufacturing organizations have been analyzed. The findings of the study will assist the professionals and practitioners in formulating policies based on the actual nature and intensity of the barriers.

Machine Learning Classification Techniques for Detecting the Impact of Human Resources Outcomes on Commercial Banks Performance

The banking industry is a market with great competition and dynamism where organizational performance becomes paramount. Different indicators can be used to measure organizational performance and sustain competitive advantage in a global marketplace. The execution of the performance indicators is usually achieved through human resources, which stand as the core element in sustaining the organization in the highly competitive marketplace. It becomes essential to effectively manage human resources strategically and align its strategies with organizational strategies. We adopted a survey research design using a quantitative approach, distributing a structured questionnaire to 305 respondents utilizing efficient sampling techniques. The prediction of bank performance is very crucial since bad performance can result in serious problems for the bank and society, such as bankruptcy and negative influence on the country’s economy. Most researchers in the past adopted traditional statistics to build prediction models; however, due to the efficiency of machine learning algorithms, a lot of researchers now apply various machine learning algorithms to various fields, including performance prediction systems. In this study, eight different machine learning algorithms were employed to build performance models to predict the prospective performance of commercial banks in Nigeria based on human resources outcomes (employee skills, attitude, and behavior) through the Python software tool with machine learning libraries and packages. The results of the analysis clearly show that human resources outcomes are crucial in achieving organizational performance, and the models built from the eight machine learning classifier algorithms in this study predict the bank performance as superior with the accuracies of 74–81%. The feature importance was computed with the package in Scikit-learn to show comparative importance or contribution of each feature in the prediction, and employee attitude is rated far more than other features. Nigeria’s bank industry should focus more on employee attitude so that the performance can be improved to outstanding class from the current superior class.

The Impact of Technological Innovation on Industry 4.0 Implementation and Sustainability: An Empirical Study on Malaysian Small and Medium Sized Enterprises

Small and Medium sized Enterprises (SMEs) are the pillars on which most of the economies worldwide rest. Without the support of qualified technological innovation, it will be very difficult for SMEs’ performance to improve and impossible for them to reach their sustainability goals. Small businesses should therefore be encouraged to embrace the next technological frontier, Industry 4.0 (I4.0). The main purpose of this research is to understand the relationship between the implementation of Industry 4.0 technologies and sustainability goals, along with an analysis of how innovation characteristics make implementing I4.0 easier for small businesses. To answer the research questions and analyse the high complex data, this research performed a structural equation model by using AMOS software. The results indicated that technology innovation characteristics have a positive significant effect on I4.0 implementation and sustainability goals. However, effective implementation of I4.0 mediates between the relationship of innovation characteristics and sustainability goals, except observability. The implications of this research are that SMEs should develop effective I4.0, implement it, and build innovation characteristics to reach sustainability goals.

The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees

Big data is rapidly being seen as a new frontier for improving organizational performance. However, it is still in its early phases of implementation in developing countries’ healthcare organizations. As data-driven insights become critical competitive advantages, it is critical to ascertain which elements influence an organization’s decision to adopt big data. The aim of this study is to propose and empirically test a theoretical framework based on technology–organization–environment (TOE) factors to identify the level of readiness of big data adoption in developing countries’ healthcare organizations. The framework empirically tested 302 Malaysian healthcare employees. The structural equation modeling was used to analyze the collected data. The results of the study demonstrated that technology, organization, and environment factors can significantly contribute towards big data adoption in healthcare organizations. However, the complexity of technology factors has shown less support for the notion. For technology practitioners, this study showed how to enhance big data adoption in healthcare organizations through TOE factors.

On relating big data analytics to supply chain planning: towards a research agenda

Purpose

This paper aims to examine how the extant publication has related big data analytics (BDA) to supply chain planning (SCP). The paper presents a conceptual model based on the reviewed articles and the dominant research gaps and outlines the research directions for future advancement.

Design/methodology/approach

Based on a systematic literature review, this study analysed 72 journal articles and reported the descriptive and thematic analysis in assessing the established body of knowledge.

Findings

This study reveals the fact that literature on relating BDA to SCP has an ambiguous use of BDA-related terminologies and a siloed view on SCP processes that primarily focuses on the short-term. Looking at the big data sources, the objective of adopting BDA and changes to SCP, we identified three roles of big data and BDA for SCP: supportive facilitator, source of empowerment and game-changer. It bridges the conversation between BDA technology for SCP and its management issues in organisations and supply chains according to the technology-organisation-environmental framework.

Research limitations/implications

This paper presents a comprehensive examination of existing literature on relating BDA to SCP. The resulted themes and research opportunities will help to advance the understanding of how BDA will reshape the future of SCP and how to manage BDA adoption towards a big data-driven SCP.

Originality/value

This study is unique in its discussion on how BDA will reshape SCP integrating the technical and managerial perspectives, which have not been discussed to date.

Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model

The purpose of this paper is to identify the factors that affect the intention to use Big Data Applications in companies. Research into Big Data usage intention and adoption is scarce and much less from the perspective of the use of these techniques in companies. That is why this research focuses on analyzing the adoption of Big Data Applications by companies. Further to a review of the literature, it is proposed to use a UTAUT model as a starting model with the update and incorporation of other variables such as resistance to use and perceived risk, and then to perform a neural network to predict this adoption. With respect to this non-parametric technique, we found that the multilayer perceptron model (MLP) for the use of Big Data Applications in companies obtains higher AUC values, and a better confusion matrix. This paper is a pioneering study using this hybrid methodology on the intention to use Big Data Applications. The result of this research has important implications for the theory and practice of adopting Big Data Applications.

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts' evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation.

Examining the Factors Affecting SME Performance: The Mediating Role of Social Media Adoption

Small and medium enterprises (SMEs) have become a vibrant and dynamic sector of the world economy. Information technology plays a vital role in improving the productivity and competitiveness of SMEs. The dynamic business environment has brought fierce competition among SMEs and, therefore, requires the owners to interact with internal and external members actively. Hence, this study aims to investigate the impact of technology, organization, and environment as important factors in the performance of small and medium-sized enterprises. It also examines the mediating role of social media adoption. Items were developed to measure the various purposes of social media use in organizations, which contribute to increasing the measurement of social media usage. For the empirical investigation, this paper used a closed-ended questionnaire. Using a quantitative method, we observed 423 responses through structural equation modeling. The findings of the study indicate that technology, organization, and the environment play effective roles for SME performance. More importantly, social media adoption positively mediates the relationship between technology, organization, environment, and SME performance. The study also helps organizations realize the advantages of using social media and specifies the rationale behind an organization’s investment in social media.

A review of challenges and opportunities of blockchain adoption for operational excellence in the UK automotive industry

Purpose

This paper aims to explore the challenges and opportunities of blockchain technology adoption from the lens of the technological–organisational–environmental (TOE) framework for operational excellence in the UK automotive industry context.

Design/methodology/approach

The research methodology of this study follows a systematic review approach, which analyses existing academic published research papers in the top 35 academic journals. There was no specific timeframe established for this study and shortlisting the articles through a set of used keywords. A sample of 71 articles was shortlisted and analysed to provide a discussion on technological and management challenges and opportunities of blockchain adoption from the lens of the TOE framework for operational excellence.

Findings

The findings of this study present significant theoretical and managerial implications and deep understanding for firms seeking to understand the challenges and opportunities of blockchain adoption for their operational excellence.

Research limitations/implications

The systematic literature approach was considered for the present study to explore existing academic papers on technological and management challenges and opportunities from the lens of TOE framework for operational excellence, whereas a more specified method meta-analysis can be considered for future research. The study has been explored in the UK automotive industry context, which has been considered as the limitation of generalisation across countries and industries.

Originality/value

This paper represents the most comprehensive literature study related to the technological and management challenges and opportunities of blockchain from the TOE framework angle for operational excellence.

Barriers to adoption of blockchain technology in green supply chain management

Purpose

The purpose of this study is to identify the barriers to the adoption of blockchain technology in green supply chain management (GSCM) and further analyze the cause and effect relationship to prioritize the barriers for making strategic decisions.

Design/methodology/approach

The study examines 15 potential barriers related to the adoption of blockchain in GSCM which is identified from the literature review and finalized after subsequent discussions with industry professionals. Integrated Fuzzy-Decision-Making Trial and Evaluation Laboratory approach is used to analyze cause and effect relationships and prioritize the barriers. Fuzzy set theory is used to handle the uncertainty and vagueness associated with the personnel biases and data deficiency problems. Three small to medium enterprises’ (SMEs’) are considered for gathering data and further analyzing the crucial barriers that are impeding the adoption of blockchain technology in GSCM.

Findings

The findings reveal that “lack of management vision” and “cultural differences among supply chain partners” are the most influencing barriers, whereas; “collaboration challenges” and “hesitation and workforce obsolescence” are the most influential barriers in the adoption of blockchain in GSCM.

Research limitations/implications

The study is developed based on 15 selected barriers which were further tested using data from three SMEs’ in the emerging economy of India. The adoption of blockchain technology in GSCM is at a nascent stage and more research studies are necessary to extend the knowledge base.

Practical implications

Managers need to eliminate the barriers and extend the blockchain technology application in GSCM. Managers need to develop the mission and vision of the company by doing proper alignment of blockchain technology with GSCM goals. Second, managers need to make strong collaborations and remove the hesitation and workforce obsolescence barrier by providing the right education and pieces of training.

Originality/value

Blockchain technology in GSCM is in a nascent stage. This study extends the knowledge base by identifying and further prioritizing the leading blockchain barriers that need to be overcome for effectively adopting blockchain in GSCM.