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This study aims to explore the factors influencing the bottom of the pyramid (BOP) consumers’ adoption and usage intention towards mobile payment (m-payment) to achieve financial inclusion and sustainable development goals.

A qualitative research design is used to explore the enablers and inhibitors that influence BOP consumers’ m-payment adoption and usage intention. To collect the qualitative responses, semi-structured in-depth interviews with BOP respondents were conducted. The thematic analysis using the text mining technique will be used to analyse qualitative data for exploring the predominant factors affecting m-payment adoption intention and usage.

The results suggested awareness, social influences and self-efficacy as crucial enablers and privacy and security risks and vulnerability concerns as crucial inhibitors towards m-payment adoption and usage.

As a novel contribution to the BOP, financial inclusion, sustainable development goals and m-payment literature, this study unfolds several unknown perceived benefits and perceived sacrifices that influence the BOP consumers’ m-payment adoption intention and usage. The study’s findings help the government and banks formulate and implement strategies to achieve financial inclusion among BOP consumers.

Inclination angle has been reported to have an enhancing effect on the thermal-hydraulic characteristics and entropy of some thermal systems. Therefore, this paper aims to numerically investigate the effects of inclination angle, volume concentration and Reynolds number on the thermal and hydraulic characteristics and entropy generation rates of water-based Al₂O₃ nanofluids through a smooth circular aluminum pipe in a turbulent flow.

A constant heat flux of 2,000 Watts is applied to the circular surface of the tube. Reynolds number is varied between 4,000 and 20,000 for different volume concentrations of alumina nanoparticles of 0.5%, 1.0% and 2.0% for tube inclination angles of ±90o, ±60o, ±45o, ±30o and 0o, respectively. The simulation is performed in an ANSYS Fluent environment using the realizable kinetic energy–epsilon turbulent model.

Results show that +45o tube orientation possesses the largest thermal deviations of 0.006% for 0.5% and 1.0% vol. concentrations for Reynolds numbers 4,000 and 12,000. −45o gives a maximum pressure deviation of −0.06% for the same condition. The heat transfer coefficient and pressure drop give maximum deviations of −0.35% and −0.39%, respectively, for 2.0% vol. concentration for Reynolds number of 20,000 and angle ±90o. A 95%–99.8% and 95%–98% increase in the heat transfer and total entropy generation rates, respectively, is observed for 2.0% volume concentration as tube orientation changes from the horizontal position upward or downward.

Research investigating the effect of inclination angle on thermal-hydraulic performance and entropy generation rates in-tube turbulent flow of nanofluid is very scarce in the literature.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

The purpose of this paper is to examine the contribution of big data in the transforming process of an IT firm to a learning organization.

The authors adopted a qualitative research approach to define and interpret the ideas and experiences of the IT firms’ employees and to present them to the readers directly. For this purpose, they followed a single-case study design. They researched on a small and medium enterprise operating in the IT sector in Düzce province, Turkey. This paper used a semi-structured interview and document analysis as data collecting methods. In all, eight interviews were conducted with employees. Brochures and website of the organization were used as data sources for the document analysis.

As a result of in-depth interviews and document analysis, the authors formed five main themes that describe perception of big data and learning organization concepts, methods and practices adopted in transforming process, usage areas of big data in organization and how the sample organization uses big data as a learning organization. The findings of this paper show that the sample organization is a learning IT firm that has used big data in transforming to learning organization and in maintaining the learning culture.

The findings contribute to literature as it is one of the first studies that examine the influence of big data on the transformation process of an IT firm to a learning organization. The findings reveal that IT firms benefit from the solutions of big data while learning. However, as the design of the research is single-case study, the findings may be specific to the sample organization. Future studies are required that examine the subject in different samples and by different research designs.

In literature, research on how IT firms’ managers and employees use big data in organizational learning process is limited. The authors expect that this paper will shed light on future research that examines the effect of big data on the learning process of the organization.

This study aims to analyse the structure of the Indian vaccine supply chain (SC) during the Covid-19 crisis and explore the underlying challenges at each stage in the network. It also brings out the difference in performance of various constituent states.

This study relied on both primary and secondary data for the analyses. For the primary data, the study gathered experts’ opinions to validate the authors’ inferences. For the secondary data, it relies on government data provided in websites.

Based on the quartile analysis and cluster analysis of the secondary data, the authors find that the constituent states responded differently during the first and second waves. This was due to the differences in SC characteristics attributed to varied demographics and administrative efficiency.

This paper’s analyses is primarily limited to secondary information and inferences are based on them. The study has important implications for implementing the large-scale vaccination drives by government and constituent states for better coordination and last-mile delivery.

The contribution is unique in studying the performance of constituent states using statistical techniques, with secondary data from authentic sources. It is also unique in combining this observation with validation from experts.

Policymakers are developing government-level pandemic response strategies (GPRS) to assist architecture, engineering and construction (AEC) enterprises. However, the effectiveness of the GPRS has not been assessed. Therefore, this study aims to investigate the interrelationships between GPRS and AEC enterprises. To achieve that aim, the study objectives are to compare GPRS effectiveness between small-medium and large AEC enterprises, develop groupings to categorize interrelated GPRS and evaluate the effectiveness of the GPRS and interrelated constructs.

A systematic literature review and semi-structured interviews with 40 AEC industry professionals were carried out, generating 22 GPRS. Then, questionnaire survey data was collected among AEC professionals. In total, 114 valid survey answers were received and analyzed using the Kruskal–Wallis H test, normalized mean analysis, factor analysis and fuzzy synthetic evaluation.

Small-medium enterprises have four distinct critical GPRS: “form a special task force to provide support in maneuvering COVID-19,” “provide infrastructure investment budgets to local governments,” “develop employee assistance programs that fit all types of working groups” and “diversify existing supply chain.” Large enterprises have two distinct critical GPRS: “provide help in digitalizing existing construction projects” and “mandate COVID-19 as force majeure.” Eighteen GPRS can be categorized into the following five constructs: “market stability and financial aid,” “enterprise capability management,” “supply chain improvement,” “law and policy resources” and “information and workforce management.” The former two constructs are more effective than other GPRS constructs.

This is the first paper that evaluates the effectiveness of GPRS for AEC enterprises, providing new evidence to policymakers for well-informed decision-making in developing pandemic response strategies.

The psychological foundations of consumers’ reasons for product choices are analyzed in the field of marketing. The purpose of this research is to identify the implicit reasons for white meat consumption in the UK and Turkey.

In the scope of the means-end chain theory, in-depth interviews were conducted with individuals, and the reasons for consumers’ product preferences were revealed by moving from concrete to abstract.

It has been determined that the white meat consumption of Muslims in the UK is primarily shaped by their religious approach. In Turkey, on the contrary, both consumption patterns and reasons for preference are changing. It has been found that white meat consumption is associated with values such as security needs, satisfaction with life, self-fulfillment and happiness.

This research has contributed to the marketing literature by examining consumers’ implicit consumption reasons for white meat in the context of religion and culture.

Marketing strategies should focus on building trust in halal certification, particularly in the UK. Brands should associate their promotion strategies with feelings of security and happiness, which are associated in the minds of consumers.

This study is a new study in terms of revealing the connotations of consumers about consuming chicken and fish and showing the implicit needs that the brands can emotionally associate with.

The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective.

This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM.

From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles.

This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.

Develop a comprehensive framework for assessing the knowledge organization systems (KOSs), including the taxonomy of Wikipedia and the ontologies of Wikidata, with a specific focus on enhancing management and retrieval with a gender nonbinary perspective.

This study employs heuristic and inspection methods to assess Wikipedia’s KOS, ensuring compliance with international standards. It evaluates the efficiency of retrieving non-masculine gender-related articles using the Catalan Wikipedian category scheme, identifying limitations. Additionally, a novel assessment of Wikidata ontologies examines their structure and coverage of gender-related properties, comparing them to Wikipedia’s taxonomy for advantages and enhancements.

This study evaluates Wikipedia’s taxonomy and Wikidata’s ontologies, establishing evaluation criteria for gender-based categorization and exploring their structural effectiveness. The evaluation process suggests that Wikidata ontologies may offer a viable solution to address Wikipedia’s categorization challenges.

The assessment of Wikipedia categories (taxonomy) based on KOS standards leads to the conclusion that there is ample room for improvement, not only in matters concerning gender identity but also in the overall KOS to enhance search and retrieval for users. These findings bear relevance for the design of tools to support information retrieval on knowledge-rich websites, as they assist users in exploring topics and concepts.

This paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.

Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.

The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.

The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.