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Neuromarketing is a marketing communication field that applies neuroscience and physiological research tools to study consumer behavior toward stimuli, viz., ads and brands. This study aims to assess research trends in the neuromarketing field on the most influential journals, authorships, countries, citations and co-occurrences. The Scopus database is used to analyze identified articles from 2013 to 2022 and for the eligible research articles, a “systematic methodological review” (SMR) on consumer behavior through neuromarketing approach was done. “Visualization of Science (VOS)” viewer and “Biblioshiny” by R-studio software have been used for mapping the keyword analysis, co-citation analysis and author occurrence analysis. It was further found that of the top 10 academic institutions, the list is dominated by the six Asian institutions. It was further witnessed that journal “Physiology and Behavior” is trending as the most dedicated and emerging journals on neuromarketing and consumer behavior. Asian nations such as Bangladesh, China, India, Indonesia, etc., are turning out to be an emerging collaborators and publishers in this niche area of research, thereby giving tough competition to most developed countries. The findings of the thematic mapping show that neuromarketing is itself a very novel and newest area of study and topics such as “human marketing,” “neuromarketing,” “consumer behavior” and “electroencephalography” are new dimensions that can be looked upon in future.

This paper aims to propose an improved version of portfolio optimization model through the prediction of the future behavior of stock returns using a combined wavelet-based long short-term memory (LSTM).

First, data are gathered and divided into two parts, namely, “past data” and “real data.” In the second stage, the wavelet transform is proposed to decompose the stock closing price time series into a set of coefficients. The derived coefficients are taken as an input to the LSTM model to predict the stock closing price time series and the “future data” is created. In the third stage, the mean-variance portfolio optimization problem (MVPOP) has iteratively been run using the “past,” “future” and “real” data sets. The epsilon-constraint method is adapted to generate the Pareto front for all three runes of MVPOP.

The real daily stock closing price time series of six stocks from the FTSE 100 between January 1, 2000, and December 30, 2020, is used to check the applicability and efficacy of the proposed approach. The comparisons of “future,” “past” and “real” Pareto fronts showed that the “future” Pareto front is closer to the “real” Pareto front. This demonstrates the efficacy and applicability of proposed approach.

Most of the classic Markowitz-based portfolio optimization models used past information to estimate the associated parameters of the stocks. This study revealed that the prediction of the future behavior of stock returns using a combined wavelet-based LSTM improved the performance of the portfolio.

The globalisation of markets, emerging concepts of sustainable development, and circular economy have defined the boundaries within which organisations must compete and address the needs of key stakeholders. As circumstances change, boundaries are often replaced by the relationships between companies and the communities they serve. Consequently, strategy has become a central aspect of sustainable leadership and the foundation for implementing strategic management in a dynamic system of relationships. Every company is born and grows within social and economic ecosystems. Drawing on the metaphor of biology, ecosystems are described as dynamic interconnections among various elements that influence and foster entrepreneurship. Interconnections between players (such as marketplaces, organisations, governments, and universities) create a flow of expertise, abilities, knowledge, experience, and tangible resources. Economic and social ecosystems involve various actors and components that continuously coexist and interact, leading to the creation of numerous mutual relationships. Consequently, it is crucial for managers to gain a comprehensive understanding of the internal and external environments. Various decision-making tools and strategies can be used to achieve this goal. These tools were developed to assist managers, researchers, and consultants in making informed decisions under complex scenarios. This chapter presents several decision-making strategies and tools, including the Boston Consulting Group (BCG) matrix, General Electric (GE) matrix, Balanced Scorecard (BSC), PEST, PESTEL analysis, and SWOT analysis.

Goal orientation shapes employees’ approach to and interpretation of workplace aspects such as supervisors’ behavior. However, research has not fully examined the effect of goal orientation as an antecedent of abusive supervision. Drawing from victim precipitation theory, this study aims to fill this research gap by investigating how employees’ goal orientation influences their perception of abusive supervision.

Two studies were conducted to test the hypotheses. In Study 1, 181 employees in 45 departments participated in the survey, and multilevel confirmatory factor analysis, two-level path model and polynomial regression were used. In Study 2, 108 working adults recruited from a professional online survey platform participated in a two-wave time-lagged survey. Confirmatory factor analysis, hierarchical linear regression and polynomial regression were used.

This study found that employees’ learning goal orientation was negatively related to their perception of abusive supervision. In contrast, performance-avoidance goal orientation was positively related to their perception of abusive supervision, whereas performance-approach goal orientation was unrelated to this perception. Moreover, employees’ perception of abusive supervision was greater when learning and performance-approach goal orientation alignment occurred at lower rather than higher levels, and when performance-avoidance and performance-approach goal orientation alignment occurred at higher rather than lower levels.

This research identified two novel victim traits as antecedents of abusive supervision – employees’ learning goal orientation and performance-avoidance goal orientation. Furthermore, adopting a multiple goal perspective, the authors examined the combined effects of goal orientation on employees’ perception of abusive supervision.

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.

Drawing on the knowledge-based view, this study investigates how IT–business alignment influences business model design via organizational learning and examines the moderating role of data-driven culture in the relationship between IT–business alignment and business model design via organizational learning.

Using multi-respondent survey data collected from 597 Chinese firms, mediation and moderated mediation analyses were used to examine this study's hypotheses.

The mediation test results revealed organizational learning served as a mediator between IT–business alignment and two types of business model design (i.e. novelty- and efficiency-centered). In addition, data-driven culture strengthened the indirect effects of IT–business alignment on these two types of business model design via organizational learning.

This study extends current understandings of the relationship between IT–business alignment and business model design by revealing the mediating role of organizational learning and investigating its indirect effects under various degrees of data-driven culture. As such, it contributes to the literature on the business model and IT–business alignment and provides insights for managers seeking to achieve the expected business model design.

The article aims to identify the determinants of the urbanization process given the magnitude of this phenomenon and also its economic, social and environmental implications and pressure on public authorities to find viable solutions in the context of sustainable development.

The research is based on regression analysis with urbanization growth being the dependent variable and enrollment in higher education, GDP, value added by industry and female labor force as independent variables.

The main factors that favor urbanization are the increase in population, industry value-added and the female labor force. Urbanization is an objective that must be pursued differently by public authorities in developed and developing countries, given the different realities they face-population growth in developing countries, and population aging and international migration in developed countries.

The present research has limitations generated by the selection of independent variables, which is why in future directions, the research will consider the use of other indicators such as the number of graduates, exports as percent of GDP or migrations as a percentage of the total population. Given the complexity of the phenomenon of urbanization, future research will focus on groups of countries as they resulted from the clustering made by this article.

The impact of higher education on urbanization is low, which is why the intensification of partnerships between municipal authorities and universities could be a solution that ensures not only the transfer of knowledge from academia to the public sector but also from entrepreneurs increasingly aware of the importance of promoting SDGs for urban development in the context of the challenges posed by global warming. The involvement of women in the labor market is essential for the intensification of the urbanization process. The increasing presence of women in the labor market generates an increase in the income of families, an increase in the level of education and an intensification of national and international migration. The research findings can be used by policy and decision-makers to develop appropriate urbanization and education policies and strategies.

The existence of inhomogeneous clusters is noticeable, which demonstrates the complexity of the urbanization phenomenon that is present in all countries around the world, but under the influence of different factors such as the export of natural resources (mainly oil) or the intensification of industrial activity. Taking into account the variables used, this study stands out in the multitude of articles published in the international mainstream.

This study examines the effect of occupational stress on turnover intention of employees working in the banking industry. The authors examine the mediating effects of service climate and emotional regulations of the employees in the relationship between occupational stress and intention to leave the organization.

This study followed stratified sampling technique for data collection from employees of ten commercial banks based on the banks' financial performance of top 5 and bottom 5 out of 27 banks. Data were collected at 2 stages, first from 465 employees for occupational stressors and second from 408 employees among the participants in the first stage for turnover intention, service climate and emotional regulation. Harman’s one-factor test was conducted to examine the common method bias. Confirmatory factor analysis (CFA), regression analysis and Preacher and Hayes Process Macro approach were used to examine mediation effect.

Three factors, namely workload (WL), role ambiguity and growth opportunity expectations were identified as the occupational stressors in the banking industry, predicting a positive relation of overall occupational stress to the intention to turnover. Service climate and the employees’ emotion regulation ability mediate the relationship between stress and turnover intention. Results also revealed no significant role of control variables in predicting occupational stress and turnover intention.

This study implies that the WL, role ambiguity and growth opportunity expectations of the employees cause stress in employees which may lead to have turnover intention. In order to get success in competitive environment, managers of banking industry can address stressors by enhancing service climate and formulating policies and programs to strengthen the emotion regulation which is evidence to strengthen the reciprocity approach of social exchange theory in employees’ commitment.

This study contributes to the social exchange theory and attempts to fulfill the gaps in empirical research on personnel psychology, human capital\ and organization management in developing countries.

Petroleum hydrocarbons are naturally occurring flammable fossil fuels used as conventional energy sources. It has carcinogenic, mutagenic properties and is considered a hazardous pollutant. Soil contaminated with petroleum hydrocarbons adversely affects the properties of soil. This paper aim to remove pollutants from the environment is an urgent need of the hour to maintain the proper functioning of soil ecosystems.

The ability of micro-organisms to degrade petroleum hydrocarbons makes it possible to use these microorganisms to clean the environment from petroleum pollution. For preparing this review, research papers and review articles related to petroleum hydrocarbons degradation by micro-organisms were collected from journals and various search engines.

Various physical and chemical methods are used for remediation of petroleum hydrocarbons contaminants. However, these methods have several disadvantages. This paper will discuss a novel understanding of petroleum hydrocarbons degradation and how micro-organisms help in petroleum-contaminated soil restoration. Bioremediation is recognized as the most environment-friendly technique for remediation. The research studies demonstrated that bacterial consortium have high biodegradation rate of petroleum hydrocarbons ranging from 83% to 89%.

Proper management of petroleum hydrocarbons pollutants from the environment is necessary because of their toxicity effects on human and environmental health.

This paper discussed novel mechanisms adopted by bacteria for biodegradation of petroleum hydrocarbons, aerobic and anaerobic biodegradation pathways, genes and enzymes involved in petroleum hydrocarbons biodegradation.

In this study, we examine whether work from home (WFH) had an impact on firm productivity during the COVID-19 period.

We employ a panel fixed-effect model using 79,201 firm-quarter observations in a cross-country setting of 68 countries.

First, we find that firms that employed WFH contributed to real sector growth during the pandemic due to greater capital expenditure compared to otherwise. Second, we find that WFH amenable firms turned over assets better than less WFH amenable firms.

To the best of our knowledge, this is the first study to examine the impact of WFH on firms’ investment and efficiency using a cross-country setting.