Search results

Discount grocery stores (DGSs) are attractive food supply chain (FSC) channels because many cost-conscious Indians use them for monthly needs. Despite capacity, DGSs must address customer concerns about store crowd densities and improve their COVID-19 preparedness. The purpose of this study is to learn how retail operations strategies can improve customer experience and how stores can benefit.

The study looked at a case study where retail operations are run more efficiently, and the customer experience is enhanced by standardizing and customizing customer transactions. The potential benefits that customers and retailers might anticipate are then statistically verified. Next, the potential benefits were examined to determine which ones from customers’ and retailers’ views should be prioritized to increase satisfaction.

The case situation analysis in the study demonstrates how DGSs can improve their retail operations to reduce customer wait times and provide greater convenience. The study also provides practitioners with potential benefits to pursue from the perspectives of retailers, customers and both retailers and customers.

This study requires many past transactions and can be considered an extension of the current study, so it does not capture floor space and capacity improvements.

This research can help FSC retailers compete with upstream supply chain partners and customers in omnichannel retailing. By improving DGS retailer capacity and customer experience, this study can benefit all FSC stakeholders.

Although there are numerous potential benefits that practitioners can pursue, the current study suggests that practitioners focus on those that can improve retailer and customer satisfaction.

This study aims to analyze the effects of microentrepreneurs’ knowledge transfer (KT) on personal improvement (PI) and business improvement (BI).

The study was developed in two stages: a literature review based on KT and the learning process in microenterprises to have managerial competence and PI and BI to acquire the managerial competence that entrepreneurs need. The second stage was constructing a structural model based on 107 questionnaires and bootstrapping of 5,000 replications of microentrepreneurs who went through a training program (quantitative) and a focus group (qualitative). This study had a mixed approach, exploratory scope and experimental design.

The research showed real evidence about the performance level of microentrepreneurs when they passed through the process of KT and its impact on PI and BI. This research considers their managerial competencies, and the findings show a relationship between the theory of individual and organizational learning.

This study considered Peruvian microentrepreneurs who participated in a virtual training program that included several courses related to their current environments and topics of interest. The analyzed period covered the years affected by COVID-19.

The model reveals that KT is relevant to PI and BI. Performance was measured regarding growth, income, innovation, productivity and responsibility before and after the program.

This research analyzed the need for training microentrepreneurs for personal and private reasons under a COVID-19 scenario to foster their businesses and assume financial responsibilities. This study considered Peru’s reality, a country in which 94.9% of companies are microenterprises. The study revealed that microentrepreneurs improved their personal and professional lives and addressed relevant social problems that affect their environments because of the KT effects.

This study bridges the gap in the literature on how the theory of KT can be applied to entrepreneurs. This study revealed significant findings in terms of PI and BIs. The impact of KT indicates the relevance of managerial competencies related to the performance level obtained in terms of growth, income, innovation, productivity and responsibility.

This paper aims to examine the return and volatility transmission among economic policy uncertainty (EPU), geopolitical risk (GPR), their interaction (EPGR) and five tradable precious metals: gold, silver, platinum, palladium and rhodium.

Applying time-varying parameter vector autoregression (TVP-VAR) frequency-based connectedness approach to a data set spanning from January 1997 to February 2023, the study analyzes return and volatility connectedness separately, providing insights into how the data, in return and volatility forms, differ across time and frequency.

The results of the return connectedness show that gold, palladium and silver are affected more by EPU in the short term, while all precious metals are influenced by GPR in the short term. EPGR exhibits strong contributions to the system due to its elevated levels of policy uncertainty and extreme global risks. Palladium shows the highest reaction to EPGR, while silver shows the lowest. Return spillovers are generally time-varying and spike during critical global events. The volatility connectedness is long-term driven, suggesting that uncertainty and risk factors influence market participants’ long-term expectations. Notable peaks in total connectedness occurred during the Global Financial Crisis and the COVID-19 pandemic, with the latter being the highest.

Using the recently updated news-based uncertainty indicators, the study examines the time and frequency connectedness between key uncertainty measures and precious metals in their returns and volatility forms using the TVP-VAR frequency-based connectedness approach.

This study aims to discover the key performance indicators (KPIs) of the agricultural cold supply chain (ACSC) and analyze their consequences on the performance of ACSC within the bounds of Indian topography.

The KPIs have been explored based on the literature review both in global and Indian context and domain expert's opinions. The interdependency characteristics and cause–effect relationship among the KPIs have been analyzed using a fuzzy decision-making trial and evaluation laboratory (f-DEMATEL) approach.

The findings extracted from the empirical assessment of the problem find strong compliance with the notions of theoretical model assessment. The results highlight that the cost of product waste and operating and performance costs are the two most important performance indicators of an Indian ACSC. Furthermore, governmental policies and regulations and the effectiveness of cold chain (CC) equipment also have a high degree of influencing characteristics on ACSC performance.

To connect the study with practicalities, the assessment of the KPIs is allied with real-time practices by clustering the beliefs of Indian professionals. Therefore, the decision-making behavior of the experts might be influenced by geographical constraints. However, the key findings provide advantages to the ACSC players, a bright hope for future food security and a significant profit for farmers.

The presented paper encompasses various aspects of the ACSC, including theoretical and empirical perspectives exercised to contemplate the system dynamics, which inculcates the essence of the associated practicalities. Thus, this study has various practical contributions relevant to managerial and societal perspectives.

Given the decisive role of knowledge-oriented leadership (KOL) in boosting organizational innovation capacities, the research intends to investigate the effect of KOL on ambidextrous innovation with the mediating effect of knowledge management capability (KMC). Furthermore, technological turbulence (TT) is regarded as a moderator in the relationship between KMC and ambidextrous innovation.

The data obtained from 122 Pakistani manufacturing firms were used to evaluate the proposed relationships using the partial least square structural equation modeling approach.

The empirical findings demonstrate that KOL positively affects both aspects of ambidextrous innovation, namely exploitative innovation (EII) and exploratory innovation (ERI), with a higher effect on EII. Additionally, knowledge management process capability (KMPC) partially mediates the association between KOL and both dimensions of ambidextrous innovation (EII and ERI). Similarly, knowledge management infrastructure capability (KMIC) mediates the link between KOL and ERI but does not mediate the relationship between KOL and EII. The impacts of the KMPC and KMIC on EII are also significantly moderated by TT, although the link between the two components of the KMC (KMPC and KMIC) and ERI is unaffected.

The research provides useful knowledge and a novel strategy for policymakers to foster KOL and invest in KMC to improve the capabilities of Pakistani manufacturing firms in terms of innovation.

The research has contributed significantly to the resources-based view and knowledge-based view (KBV) literature by examining the various mediation moderation mechanisms and offering greater insights into the relationship between KOL and firms, KMC, and ambidextrous innovations.

This paper aims to discuss the emergence of science, technology and innovation (STI) institutions in Indonesia during the Dutch East Indies colonial period in 1778–1941. The emergence of these institutions reflected the dynamics of science and technology development and innovation in that era.

This paper navigates a historiographical approach. Data collection techniques use “secondary data research,” with archival investigation published by official sources in the Dutch East Indies in the 18th and 19th centuries as well as other reference sources, and data analysis techniques use “supplementary analysis.”

This research indicates that the STI institution during the Dutch East Indies colonial period was formed to maximize the natural resources of the Dutch East Indies. The STI institution at that time was constructed as part of Buitenzorg’s Plantentuin the lands, plantations, solutions for health, astronomy, geology, forestry and culture.

The limitations of this research, as well as future research. Relying too much on “secondary data” is a limitation of this study. Therefore, it is necessary to collect primary data through in-depth interviews with historical scientists studying STIs in Indonesia in future research.

This study, to the best of the authors’ knowledge, considered the first study, reveals the dynamics of STI in Indonesia during the Dutch East Indies colonial era by examining the dynamics of the institution. In addition, this study succeeded in dividing five institutional STI clusters in the Dutch East Indies Colonial period 1778–1941, namely, units/institutions formed as part of Planuntungin te Buitenzorg; units/institutions formed based on plantations, initiated by private plantations to find solutions to the pests and diseases that attack their crops; units/institutions formed to seek solutions in the health sector; units/institutions formed based on astronomy, geology and forestry; and units/ institutions regarded as scientific councils/associations.

The evolution from emerging technologies to smart libraries is thoroughly analyzed thematically and bibliometrically in this research study, spanning 2013 through 2022. Finding and analyzing the significant changes, patterns and trends in the subject as they are represented in academic papers is the goal of this research.

Using bibliometric methodologies, this study gathered and examined a large corpus of research papers, conference papers and related material from several academic databases.

Starting with Artificial Intelligence (AI), the Internet of Things (IoT), Big Data (BD), Augmentation Reality/Virtual Reality and Blockchain Technology (BT), the study discusses the advent of new technologies and their effects on libraries. Using bibliometric analysis, this study looks at the evolution of publications over time, the geographic distribution of research and the most active institutions and writers in the area. A thematic analysis is also carried out to pinpoint the critical areas of study and trends in emerging technologies and smart libraries. Some emerging themes are information retrieval, personalized recommendations, intelligent data analytics, connected library spaces, real-time information access, augmented reality/virtual reality applications in libraries and strategies, digital literacy and inclusivity.

This study offers a thorough overview of the research environment by combining bibliometric and thematic analysis, illustrating the development of theories and concepts during the last ten years. The results of this study helps in understanding the trends and future research directions in emerging technologies and smart libraries. This study is an excellent source of information for academics, practitioners and policymakers involved in developing and applying cutting-edge technology in library environments.

This paper delves into evaluating the effectiveness of warning messages as a deterrent against excessive fat consumption. It examines how consumers perceive the fat content of food products when presented with two distinct label types: (1) a textual warning, providing succinct information about the fat content, and (2) a pictorial warning, offering a visual representation that immediately signifies the fat content.

Two quantitative studies were carried out. Study 1 employed a questionnaire to evaluate the efficacy of textual and pictorial warning messages on high- and low-fat food products. Similarly, Study 2 replicated this comparison while incorporating a neuromarketing instrument to gauge participants’ cerebral reactions.

Results indicate that pictorial warnings on high-fat foods significantly deter consumers’ purchasing intentions. Notably, these pictorial warnings stimulate the left prefrontal area of the cerebral cortex, inducing negative emotions in consumers and driving them away from high-fat food items.

While the influence of images over text in shaping consumer decisions is well understood in marketing, this study accentuates the underlying mechanism of such an impact through the elicitation of negative emotions. By understanding this emotional pathway, the paper presents fresh academic and managerial perspectives, underscoring the potency of pictorial warnings in guiding consumers towards healthier food choices.

1. Textual warnings do not seem to discourage high-fat product consumption.

2. A pictorial warning represents the fat content of an equivalent product.

3. Pictorial warnings decrease the intention to purchase a high-fat product.

4. Pictorial warnings determine an increase in negative emotions.

Textual warnings do not seem to discourage high-fat product consumption.

A pictorial warning represents the fat content of an equivalent product.

Pictorial warnings decrease the intention to purchase a high-fat product.

Pictorial warnings determine an increase in negative emotions.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.

This study aims to use a theoretical-based case study of two distinct ownership groups of the Jack Daniel’s brand to explore how rhetorical history (i.e. malleability of the past for strategic goals) may evoke and capitalize on different forms of nostalgia. Within, the authors configure four forms of nostalgia (i.e. personal, historical, collective and cultural) from the individual or collective interaction and level of direct experience one has with the past as lived or happened.

This study uses an historical research approach which involved the identification of primary and secondary sources, facility tour, source criticism and triangulation to create themes of rhetorical history infused with nostalgic narratives using compelling evidence through rich description of this fusion.

The findings reveal how nostalgia-driven narratives reflecting different collective longing for the re-creation of an American Paradise Lost used by Jack Daniel (i.e. the man) and later but differently by Brown-Forman. This study uncovers how the company’s inherited past was used rhetorically throughout its history, beginning with the nostalgic story of Jack Daniel and the distillery’s nostalgically choreographed location in Lynchburg, Tennessee. This study delves into this setting to highlight the importance of symbols, details, emotional appeals and communications for collective memory and identity development and to showcase the ways in which they are influenced by different types and forms of nostalgia.

This study adds to a limited number of studies focused on understanding the impact of founders on an organization’s brand and how that is malleable. This study responds to scholarly calls to study the influence of sequenced historical rhetoric on an organization and highlight the relevance of social emotions such as nostalgia for rhetorical history. Finally, the theoretical contribution involves the advancing and construction of a theory typology of nostalgia previously proposed by Havlena and Holak in 1996.