Search results

Major cereal staples such as wheat, white rice and corn have a significant negative impact on the environment, a low nutritional profile and are associated with obesity. In comparison, alternative staples (such as rye, quinoa, buckwheat, etc.) are more environmentally sustainable and nutritious, yet are underused. There has been a recent surge in research into and awareness of alternative staples, but the current understanding of the different drivers of and barriers to their consumption remains fragmented.

The present study attempts to assimilate and incorporate the current knowledge on the drivers of and barriers to the consumption of alternative staples. Eighty-one empirical studies were curated and analysed according to stringent protocols in order to examine the existing research profile and themes arising from prior research in this domain.

The study presents a profile of the extensive existing literature examining the drivers of and barriers to the consumption of alternative staples. The thematic analysis of selected studies resulted in the identification of six drivers and seven barriers. The drivers are an awareness of health; awareness of environmental factors; recommendations; awareness of the brand, labels and source of origin; household structure and demographic attributes. The barriers are difficulty in preparation, lack of familiarity, lack of availability, lack of affordability, culture, product attributes and sensory attributes. The various research gaps and avenues for future research associated with the drivers and barriers identified are also presented.

The key outcomes of the study are the presentation of the research profile, the identification of various drivers and barriers, the recognition of gaps in the research and avenues for future research and, finally, the development of a theoretical framework entitled “Behavioral reasoning towards the consumption of alternative staples (BRCAS)”. The study offers various insights for nutritionists, marketers, policymakers and consumers by increasing awareness of alternative staples.

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.

Climate change and digitisation are unquestionably the two defining features of this era. Both present immense challenges with unimaginable consequences for humankind while promising enormous rewards for those who can adequately address their adverse effects. These two critical factors must be considered while establishing strategies for the businesses' future operations. Hence, post-pandemic, especially with the rise of online commerce, packages and documents are delivered around the globe nearly every day, propelling the logistics industry's growth. This is not the critical challenge in logistics, the issue of sustainability, particularly as the returns are increasing exponentially, leading to a significant impact on transportation is and its reliance on fossil fuel has made it a prime target for society's growing environmental concerns. Thus, real-time visibility, collaboration and integration in reverse logistics (RL) are imperative for business sustainability. The most applicable Industry 4.0 technologies in RL are the Internet of Things (IoT), cloud computing, blockchain and digital twin that enable the defragmentation of the RL market.

This chapter analyses the technological impact of Industry 4.0 on RL. This research investigates the challenges faced by the logistics industry in the context of sustainability and how digital transformation can bring many potential benefits across the entire value chain. This chapter also presents a guidance for a framework based on the literature review that tends to favour the development of elastic logistics, implying improved company responsiveness to market conditions. The study contributes to the body of literature and the establishment of the framework for planning on the application of various Industry 4.0 technologies in developing eco-friendly and sustainable reverse logistics framework.

Service failure and recovery encounters are often witnessed by other customers, but little is known about how these encounters impacts other customers. With an aim to bridge this gap, the purpose of the paper is to explore why and how service recovery directed at a focal customer impacts other customers who are present in the same service environment.

The paper follows a qualitative research methodology. Purposive sampling was used to collect data from 30 customers through semi-structured face-to-face interviews. The data was analysed using thematic analysis.

Findings of the study show that service recovery directed at a focal customer has an impact on other customers' fairness judgments, emotions, service quality perceptions and behaviour. Other customers' behavioural reactions in response to observed service recovery are driven by two different motives: self-interest and moral obligation. Observing customers' cautious behaviour and (re)purchase behaviour are found to be primarily driven by self-interest, whereas their helping behaviour, punishment behaviour and word-of-mouth behaviour are found to be driven by moral obligation.

This study findings contribute to theory development on “other-oriented” effects of service recovery and provides valuable insights for effective management of service failures in the shared service environment.

This is the first study, which qualitatively explores the “other customers” perspective of service recovery in the context of shared servicescape.

The objective of the study is to test a conceptual model based on the interrelation between data-driven supply chain quality management practices (DDSCQMP) and the performance of organized retailing firms in India.

Based on a comprehensive review of literature, the dimensions of DDSCQMP concerning the Indian organized retail sector have been extracted. Considering the research objectives, the research data has been collected using a structured questionnaire from Indian retailers. Overall 133 questionnaires were responded successfully from retailers. The model was tested using structured equation modeling (SEM) through PLS 3.0.

The research findings confirm hypotheses and reveal the statistically significant relationship between DDSCQMP and retailers' performance at an aggregate level. However, the results of the individual-level analysis of DDSCQMP appear to vary from practice to practice. Among various DDSCQMP, “customer focus” with the highest beta (ß) value was found to have the greatest impact on performance followed by “employee relations”.

The study provides empirical justification for a structural model that identifies a positive and significant relationship between DDSCQMP and organizational performance within the context of organized retail sector of India.

This study analyzes the trends and patterns of strategic and innovative macroeconomic variables during recession or slowdown periods of 10 countries – Brazil, the United States, UK, Germany, France, China, Japan, India, Saudi Arabia, and South Korea for the period 1980–2018, which includes major recessions like the 1982 debt crisis, 1991 economic crisis, 1997 Asian Financial crisis, and 2008 Sub-prime crisis. This study devised two models – Logistic Regression and a Range-based Custom-Made Recession-cum-Economy State indicator, based on Dynamic Ordinary Least Squares (DOLS) model parameters. Results for the logistic regression model show that most of the marginal effect values are positive for variables linked with globalization indicating that increase in adverse impact on such variables increases the probability of recession. The custom-made statistical index provides an individual country-wise range, along with a global range for the weighted total of the variables, the weights for which are derived from the DOLS model, which has a 59.66% accuracy in estimating the condition of an economy. The recent worldwide experience indicates that probability of recessions has decreased, and slowdowns have increased over a period. This is evident from the Cumulative Trend of Economic State analysis that indicates that the recession probabilities of countries have decreased 1990 and that of slowdown have become highly volatile.

The purpose of this paper is to identify dimensions of competitiveness, factors affecting the competitiveness and building the framework of competitiveness for the Indian auto-component industry and further develop and validate a survey instrument based on the identified factors.

Dimensions of competitiveness and factors affecting it are extracted out after extensive literature review. A theoretical framework is developed using these factors. A survey instrument is developed based on the theoretical framework and validated through a pilot survey.

In total, 30 variables are found to be reliable in establishing the potential indicators of competitiveness. There are three significant contributions to the theory of competitiveness. It provides a theoretical framework of competitiveness to address the current market conditions of volatility. Second, it incorporates the dimensions like supply chain management, presence of global value chains and employee empowerment. Third, it clearly identifies the dimensions of competitiveness relevant in current context, like ethical behavior of firms, protection of intellectual property and innovation.

The proposed approach provides a good basis for assessing the competitive performance of the companies. This can help researchers and practitioners in deciding how to improve the competitiveness of a company.

The research proposes a theoretical framework for measuring the competitiveness of firms from a specific industry. This study indicates the factors affecting the competitiveness of Indian auto-component industry. The findings can be useful for both researchers and practitioners.

The purpose of this paper is to describe the issues, tradeoffs, and models relating to two important sets of problems that arise in supply chain management – complexities in extended supply chains and network coordination in globally dispersed supply chains. This paper highlights the role of logistics in enhancing the competitiveness of firms that operate a global supply chain.

The methodology used in this paper encompasses conceptual research and detailed literature review of key issues.

This review indicates that the key challenges faced by supply chain managers due to extended supply chains are non‐stationary demand, variability propagation, and inventory imbalances. For network coordination managers must determine the role of facilities in a global network, identify the optimal location and capacity of facilities as well as role of consolidation hubs. For the above challenges, the tradeoffs in terms of four key drivers of supply chain performance – transportation, inventory, information, and facilities and relate these to key measures of supply chain performance are described. Important directions for future research are also identified.

Test cases are needed to validate and refine the framework presented. Developing case studies that gather appropriate data to test out the models described would be important.

Companies with a global supply chain as well as third party logistics companies will find the framework presented in this paper very useful.

A new integrated framework that incorporates key decision issues like complexities of extended supply chains and network coordination into the firm's decision making has been presented. This has not been reported in previous research.

One of the most hyped concepts in the manufacturing industry is ‘Industry 4.0’. The ‘Industry 4.0’ concept is grabbing the attention of every manufacturing industry across the globe because of its immense applications. This phenomenon is an advanced version of Industry 3.0, combining manufacturing processes and the latest Internet of Things (IoT) technologies. The main advantage of this paradigm shift is efficiency and efficacy in the manufacturing process with the help of advanced automated technologies. The concept of ‘Industry 4.0’ is contemporary, so it falls under exploratory study. Therefore, the research methodology is thematic narration grounded on secondary data (online) analysis. In this light, this chapter aims to explain ‘Industry 4.0’ in terms of concepts, theories and models based on the Web of Science (WoS) database. The data include research manuscripts, book chapters, blogs, white papers, news items and proceedings. The study details the latest technologies behind the ‘Industry 4.0’ phenomenon, different business intelligence technologies and their practical implications in some manufacturing industries. This chapter mainly elaborates on Industry 4.0 frameworks designed by (1) PwC (2) IBM (3) Frost & Sullivan.