This study conducted a comprehensive review of big data supply chain analytics (BDSCA). The paper explored the application of big data in supply chain management and its benefits for organisations and society. The paper also examined the ethical, security, privacy and operational challenges of big data techniques, as well as the potential reputational damages to businesses. The review outlined four principal facets, namely: Big data analytics, applications, ethics and privacy issues, and how organizations employed this emerging tool to anticipate and even predict the future and direct their operations. These principle facets are built across the multiple levels and unique conceptual standpoints indicated by 7 themes and 14 sub-themes. These themes were generated based on 120 articles (2005À2020) drawn mainly from leading academic journals. Overall, there is a considerable consensus across current literature that big data analytics extend far beyond just reinventing the supply chain. It has the potential to support more responsive next-generation of global companies who are operating in an increasingly challenging and uncertain environment.

Logistics has become an important field as the volume of world commerce expands. The World Bank (WB) has been publishing the Logistics Performance Index (LPI) for most of the countries since 2007. LPI is accepted as an important indicator of logistical performance. In this study, a model is proposed to evaluate the LPI of the OECD countries within a specific time frame. With the proposed model, the logistical performance of OECD countries between the years 2010-2018 is analyzed and compared with the existing LPI rankings. The index is calculated using six indicators. Different from the WB survey, the fuzzy analytical hierarchy method is used to determine the weighting scores of these six indicators. The grey numbers give the researcher an opportunity to obtain the numerical expressions of a time period by showing minimum and maximum values. Thus, grey additive ratio assessment (ARAS-G) method is used to evaluate the logistics performances of OECD countries by years. The data created in this study refers to the logistics performances of the OECD countries between the years 2010 and 2018. Thus, OECD countries are ranked according to the logistics performances calculated by the ARAS-G method. The rankings calculated by ARAS-G are compared to the yearly rankings calculated by the WB. Spearman ρ and Kendall's Tau correlation methods are used to investigate the relationships within the yearly rankings and the rankings calculated for the period between 2010 and 2018 by using ARAS-G. The results show that the rankings calculated by ARAS-G have the strongest relationship with years. Indeed, this study provides a different field of study for the ARAS-G method application.

Due to the impact of the COVID-19 pandemic, on-demand grocery delivery service that combines mobile technology and city logistics has gained tremendous popularity among grocery shoppers as a substitute to self-service grocery shopping in the store. This paper proposes an intelligent comparative approach where fuzzy logic and the analytical hierarchy process (AHP) method are combined to determine the importance weights of the criteria for marketing mix elements (7Ps) of the on-demand grocery delivery service for the period before COVID-19 and during COVID-19. In addition to its comprehensive theoretical insight, this paper provides a practical contribution to decision makers who create a marketing mix for the on-demand grocery delivery service and other similar online grocery businesses in terms of efficient allocation of resources to the development of marketing mix elements. The study’s findings can also provide clues for the decision makers in times of similar pandemics and crises that are likely to be seen in the future.

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Consistent and inexpensive energy access is pivotal for human development and sustainable living standard. Pakistan is facing a huge gap in the demand and supply of energy which is brewing since 2007. This study analysed the factor influencing the households’ willingness to pay for the alternative energy source. Using a world bank data, collected from 8500 households our study presented the findings of the ordered logit model. Our results indicate that there exists a positive willingness to pay for energy alternatives for socio-economic characteristics and energy attributes. Besides, it indicates the affordability and cost-saving factor of potential alternative significantly influence the WTP for the alternative energy source. We suggested the policymakers to make policies to encourage the invention of the affordable energy products that result in reliable and long-running energy alternatives. Similarly, the financial assistance in the form of easily accessible loans, subsidies, and attractive instalments for the energy products.

The development of internet technology has affected spread of e-learning programs. Students have been attending e-learning programs worldwide. For example, students in Turkey have increasingly been using this technology to attend e-MBA programs. Due to constraints like lecturers, ability to use technology and the infrastructure of information technologies in current education systems it is not possible to reach high levels of higher education. In this paper, the authors measure lecturers’ behaviors toward e-learning activities. The study gauges their ability to use information technology, perceptions about the advantages of e-learning program, and readiness for the transition. Data are provided from a survey conducted in the School of Business Administration at Istanbul University. Descriptive statistics and nonparametric tests are used to analyze the data. These tests measure the digital divide between academicians taking into account their academic qualifications, gender and departments.