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This paper aims to identify various macroeconomic variables that affect the stock market performance of developed and emerging economies. It also investigates the effect of these factors on the stock markets of both economies. The impact of these variables on broad market indices and sectoral indices is investigated and compared too.

The publications for the study were retrieved from databases such as Emerald Insight, EBSCO, ScienceDirect and JSTOR using the keywords “Macroeconomic variables” and “Stock market” or “Stock market performance.” The result demonstrated a growing corpus of scholarly work in the domain of stock market. The study was carried out separately for each macroeconomic indicator. Given a large number of articles under consideration, the authors began by reading the titles and abstracts of all publications to identify those that were relevant. The papers are evaluated in Excel and the articles for review range from 1972 to 2021.

The authors found that gross domestic product (GDP), FDI (Foreign Direct Investment) and FII (Foreign Institutional Investment) have a positive effect on both emerging and developed economies’ stock market while gold price has a negative effect. Interest rates had a negative impact on both economies except for a few developing countries. The relationship with oil prices was positive for oil exporting countries while negative for oil importing countries. Inflation, money supply and GDP are the macroeconomic variables that have the same effect on sectoral indices as they do on broad market indices. The impact was sector-specific for the remaining variables.

This paper gives an overview of relation and effect covering variety of macroeconomic variables and stock market indices. Still, there is a scope for further research to analyze the effect on thematic, strategy and sectoral indices. A longer time horizon with new variables, such as bank deposit growth rate, nonperforming assets of banks, consumer confidence index and investor sentiment, can be studied using high-frequency data. This research may help stakeholders adopt and manage their policies during a crisis or economic slump.

This study will assist investors, researchers and educators in the fields of economics and finance in understanding how macroeconomic factors affect the stock market. Furthermore, this study can guide in portfolio diversification strategy across multiple sectors by examining the impact of macroeconomic factors specific to sectoral indices. This paper provides insight into society and researchers since it integrates a number of macroeconomic variables and their interaction with the stock market. It may also help pension funds and mutual fund firms to hedge their funds and allocate equity portfolios.

With respect to India, this study looked at new macroeconomic variables and sectors. It contrasted the impact of these variables in developed and developing economies. The effect of broad and sectoral stock indexes was also investigated and compared. The authors examined how these variables responded during crisis and economic downturns by using articles from a longer time frame. This research also looked into how changing the frequency of data for the variables altered stock performance. This paper emphasized the need for more research into thematic, strategy and broad market indices, such as small-cap and mid-cap indices.

In the ever‐changing world, vendor selection and evaluation are very important in supply chain management. Recently, there has been extensive research in the areas of vendor selection and evaluation under certainty/uncertainty without time axis, but there has been very little research in the selection of vendor with time axis. The purpose of this paper is to address this gap in the research.

Traditional approaches have been neglecting multi‐period planning horizon for vendor selection, and many decision makers or experts select vendors based on their practice and intuition. To overcome these problems, a dynamic model supporting vendors with time axis has been developed which is not always crisp, rather it involves a high degree of fuzziness and uncertainty. The authors use fuzzy analytic hierarchy process (AHP) to propose the decision model.

The research provides a mathematical system that captures the uncertainties associated with human cognitive processes in order to select the vendor. The findings of this study provide meaningful and advanced knowledge to decision makers by demonstrating a simple, efficient method to enhance the ability to predict an appropriate vendor period wise.

This research provides detailed step‐by‐step procedures to choose the best vendor selection and evaluation under uncertainty with time axis in a supply chain. It will be of value to other researchers and the department members in any firm who are responsible for selecting the vendor.

The purpose of this paper is to explore the talent acquisition practices adopted by startups and understand how small entrepreneurs leverage social capital to address the talent acquisition challenges faced by them, and; identify some of the unique parameters adopted by startups in talent acquisition.

This study uses a multiple case study method to explore the talent acquisition practices in startups in India. The study included six case studies on startups in Hubli city of North Karnataka in India.

This paper finds that startups (especially in smaller cities) face challenges such as lower quality of talent pool, absence of a brand name, inability to provide competitive salary and other benefits as per industry standards and locational disadvantages in talent acquisition. Thus, entrepreneurs leverage their social capital for talent acquisition by handpicking talent on the basis of familiarity or credible networks and recommendations. Incubation centres provide institutionalized sources of social capital to help them attract good talent. This study finds that employee-culture fit and trust play important role in acquiring talent in startups.

The study has implications for startup entrepreneurs, recruitment service providers, incubation centres, trainers, policymakers, etc. The study provides useful insights to the startups with regard to their recruitment practices.

This study contributes to the literature in the domains of talent acquisition, startups and social capital by describing hiring challenges faced by startups and exploring the mechanisms used by them in overcoming such challenges.

This paper aims to explore the role of source credibility while purchasing environment-friendly products using Ajzen’s (1991) theory of planned behavior as underpinning model.

The proposed theoretical model was empirically tested with the data collected from 334 respondents using structural equation modeling.

The results gave empirical support to the addition of source credibility to the original theory of planned. Moreover, consumer attitude was found mediating the effect of corporations’ credibility on purchase intention. Also, attitude and perceived behavioral control were found as the most important predictors of consumer’s intention to purchase environment-friendly products.

This study provides valuable insights for the marketers engaged in sustainable business practices. Amid, ever-increasing carbon emission, promoting the use of environment-friendly products has become the need of the time. Credibility plays a crucial role while promoting and communicating an organization’s sustainable practices among its stakeholders including consumers. Therefore, the marketer should formulate appropriate marketing communication strategy to communicate the consumer about the green practices and environment-friendly products they produce. The results suggest that corporation’s credibility shapes consumer attitude and influences intention to purchase environment-friendly products. Earning trust of the consumer is pivotal to achieve success in the market. Therefore, results may help the marketers to better understand consumer’s response toward their marketing strategies and further convince and persuade them to buy their products.

The findings of this study may be useful for marketers, strategists, policymakers and government while formulating promotional strategies to make consumer aware, educate and persuade them to purchase products which do not cause harm to the environment.

The study is novel in terms of exploring role of source credibility and extending theory of planned behavior in the context of sustainable consumption.

The purpose of this paper is to optimise the job shop scheduling problem using simulation and genetic algorithm.

The paper presents a simulation‐based genetic algorithm approach for the job shop scheduling problem. In total, three cases have been considered to access the performance of the job shop, with an objective to minimise mean tardiness and makespan. A restart scheme is embedded into regular genetic algorithm in order to avoid premature convergence.

Simulation‐based genetic algorithm can be used for job shop scheduling problems. Moreover, a restart scheme embedded into a regular genetic algorithm results in improvement in the fitness value. Single process plans selected on the basis of minimum production time criterion results in improved shop performance, as compared to single process plans selected randomly. Moreover, availability of multiple process plans during scheduling improves system performance measures.

The paper presents a simulation‐based genetic algorithm approach for job shop scheduling problem, with and without restart scheme. In this paper the effect of multiple process plans over single process plans, as well as criterion for selection of single process plans, are studied. The findings should be taken into account while designing scheduling systems for job shop environments.

This paper aims to determine a scale for measuring psychological factors of apparel-buying intention for young Indian online shoppers.

Churchill’s three-stage systematic scale-development methodology is used to develop a psychometric scale. Items were generated and selected in Phase I, followed by scale refinement in Phase II and scale validation in Phase III.

The final scientifically validated scale has 36 item scales that measure 10 psychological factors of apparel online-buying intention for online shoppers, from which “perceived value” emerged as the most significant factor.

This scale is a sector-specific scale that cannot be generalized to other sectors; therefore, further iterations/customizations should be made in future studies for applicability in other sectors.

This reliable and valid scale will help marketing managers to understand online shopper behavior and formulate effective strategies for online shoppers.

This paper, to the author’s knowledge, is the first attempt to develop a validated tool to measure the psychological factors of apparel-buying intention for young Indian online shoppers. This scale encompasses all important touch-points in measuring psychological factors influencing online buyer behavior for apparel products.

This study initially aims to identify the barriers to the big data analytics (BDA) initiative and further evaluates the barriers for knowing their interrelations and priority in improving the performance of manufacturing firms.

A total of 15 barriers to BDA adoption were identified through literature review and expert opinions. Data were collected from three types of industries: automotive, machine tools and electronics manufacturers in India. The grey-decision-making trial and evaluation laboratory (DEMATEL) method was employed to explore the cause–effect relationship amongst barriers. Further, the barrier's influences were outranked and cross-validated through analytic network process (ANP).

The results showed that “lack of data storage facility”, “lack of IT infrastructure”, “lack of organisational strategy” and “uncertain about benefits and long terms usage” were most common barriers to adopt BDA practices in all three industries.

The findings of the study can assist service providers, industrial managers and government organisations in understanding the barriers and subsequently evaluating interrelationships and ranks of barriers in the successful adoption of BDA in a manufacturing organisation context.

The paper is one of the initial efforts in evaluating the barriers to BDA in improving the performance of manufacturing firms in India.

This paper aims to improve upon the performance of squeeze film between porous conical plates.

The objectives are achieved by mathematically modeling a magnetic fluid based squeeze film between porous conical plates. The standard approach is to solve associated Reynolds' equation with appropriate boundary conditions. The scope of this paper is the industrial applications with regard to enhanced performance of the bearing system.

Definitely the performance of the bearing with the magnetic fluid lubricant is relatively better than the conventional lubricant. The findings indicate that the negative effect induced by the porosity can be neutralized by the positive effect caused by the magnetization parameter. Further, this paper suggests that there is scope for enhancing the performance of the bearing system by choosing a suitable combination of the magnetization parameter and semi‐vertical angle.

From the industry point of view this investigation will be certainly useful for improving the performance of the squeeze film between porous conical plates.

This paper presents the augmented performance of the squeeze film between porous conical plates and thereby extending even the life period of the machines.

The volume of data being generated by various sectors in recent years has increased exponentially. Consequently, professionals struggle to process essential data in the current competitive world. The purpose of the study is to explore and provide insights into the Big Data Analytics (BDA) studies in different sectors.

This study performs a systematic literature review (SLR) with bibliometric analysis of BDA adoption (BDAA) in the supply chain and its applications in various sectors from 2014 to 2018. This paper focuses on BDAA studies have been carried out across different countries and sectors. Also, the paper explores different tools and techniques used in BDAA studies.

The benefits of adopting BDA, coupled with a lack of adequate research in the field, have motivated this study. This literature review categorizes paper into seven main areas and found that most of the studies were carried out in manufacturing and service.

This research insight and observations can provide practitioners and academia with guidance on implementing BDA in different sustainable supply chain sectors. The article indicates a few remarkable gaps in the future direction and trends regarding the integration of BDA and sustainable supply chain development.

The study derives a new categorization of BDA, which investigates how data is generated, organized, captured, interpreted and evaluated to give valuable insights to manage the sustainable supply chain.

This study aims to investigate the mediating role of “Big Data Analytics” played between “Project Performance” and nine factors including top management, project knowledge management focus on sustainability, green purchasing, environmental technologies, social responsibility, project operational capabilities, project complexity, collaboration and explorative learning, and project success.

A sample of 321 responses from 106 Indian manufacturing small and medium-scaled enterprises (SMEs) was collected. Data were analyzed using empirical analysis through structural equation modeling.

The result shows that project knowledge management, green purchasing and project operational capabilities require the mediating support of big data analytics. The adoption of big data analytics has a positive influence on project performance in the manufacturing sector.

This study is useful to SMEs managers, practitioners and government policymakers to develop an understanding of big data analytics, eliminate challenges in the adoption of big data, and formulate strategies to handle projects efficiently in SMEs in the context of Indian manufacturing.

For the first time, big data for manufacturing firms handing innovative projects was discussed in the Indian SME context.