Journal of Modelling in Management最新文献

Purpose

The single-supplier multi-retailer cold chain is a widely adopted type of supply chain in the real-world food industry. This paper aims to consider the problem of effectively designing and managing a single-supplier multi-retailer cold chain for fresh produce with deterministic demand to minimize the total cost, which includes cooling, loss of value and carbon emission costs.

Design/methodology/approach

The global stability index (GSI) method and the non-Arrhenius model are integrated to describe the behavior of food quality degradation. The power-of-two (PoT) policy is adopted in determining the coordinated replenishment policies for the suppliers and retailers, and an appropriate wholesale price structure that can achieve the coordination of the chain is presented.

Findings

The properties of the cold chain are uncovered, and an appropriate wholesale price scheme that achieves chain coordination with the optimal PoT decision is provided. In the numerical examples, different scenarios are investigated, and it is found that the cold chain parameters influence the optimal decisions in certain ways.

Originality/value

The PoT policy – an efficient policy to determine the replenishment strategy – has not been adopted in finding the solution of a single-supplier multi-retailer cold chain in the literature. Also, no study has compared the uncoordinated and coordinated cold chain. Moreover, in the existing literature, the wholesale price is usually a constant rather than having a coordinated scheme. This research aims to fill these research gaps.

Purpose

The concept of productive entrepreneurship has been recognised as a strategic approach to address the various challenges economies face, such as high unemployment, low economic growth and limited diversification. However, studies on the productive entrepreneurship’s critical success factors (CSFs) are rare and fragmented. Hence, this paper aims to identify the CSFs of productive entrepreneurship and determine their relationship among to offer a meaningful framework for enhancing the result of entrepreneurial activities in the emerging economy.

Design/methodology/approach

The authors conducted an extensive literature review and consulted experts to identify 14 CSFs for productive entrepreneurship. The technique of interpretive structural modelling (ISM) was used to determine the relationships and interconnectedness between these factors. In addition, this study used matrix of cross-impacts applied to a classification (MICMAC) analysis to determine the significance of CSFs in relation to the productive entrepreneurship.

Findings

The results indicate that the regulatory environment, incubators and accelerators and mentorship were the most influential factors for productive entrepreneurship in the Indian context. In contrast, social mobility and resilience were found to be the least influential factor.

Research limitations/implications

The study's findings can enable researchers, policymakers and entrepreneurs to make informed decisions and develop effective strategies to enhance the productive entrepreneurship.

Originality/value

The unique approach of research (ISM and MICMAC analysis) expands the frameworks of the entrepreneurship ecosystem with a comprehensive and dynamic emergent investigation into the foundation of productive entrepreneurship.

Purpose

As some data to evaluate the efficiency of bank branches is qualitative or uncertain, only grey numbers should be used to calculate the efficiency interval. The combination of multi-stage models and grey data can lead to a more accurate and realistic evaluation to assess the performance of bank branches. This study aims to compute the efficiency of each branch of the bank as a grey number and to group all branches into four grey efficiency areas.

Design/methodology/approach

The key performance indicators are identified based on the balanced scorecard and previous research studies. They are included in the two-stage grey data envelopment analysis (DEA) model. The model is run using the GAMS program. The grey efficiencies are calculated and bank branches have been grouped based on efficiency kernel number and efficiency greyness degree.

Findings

As policies and management approaches for branches with less uncertainty in efficiency are different from branches with more uncertainty, considering the uncertainty of efficiency values of branches may be helpful for the policy-making of managers. The grey efficiency of branches of one bank is examined in this study using the two-stage grey DEA throughout one year. The branches are grouped based on kernel and greyness value of efficiency, and the findings show that considering the uncertainty of data makes the results more consistent with the real situation.

Originality/value

The performance of bank branches is modeled as a two-stage grey DEA, in which the efficiency value of each branch is obtained as a grey number. The main originality of this paper is to group the bank branches based on two grey indexes named “kernel number” and “greyness degree” of grey efficiency value.

Purpose

The purpose of this study is to propose a systematic model selection procedure for long-term load forecasting (LTLF) for ex-ante and ex-post cases considering uncertainty in exogenous predictors.

Design/methodology/approach

The different variants of regression models, namely, Polynomial Regression (PR), Generalised Additive Model (GAM), Quantile Polynomial Regression (QPR) and Quantile Spline Regression (QSR), incorporating uncertainty in exogenous predictors like population, Real Gross State Product (RGSP) and Real Per Capita Income (RPCI), temperature and indicators of breakpoints and calendar effects, are considered for LTLF. Initially, the Backward Feature Elimination procedure is used to identify the optimal set of predictors for LTLF. Then, the consistency in model accuracies is evaluated using point and probabilistic forecast error metrics for ex-ante and ex-post cases.

Findings

From this study, it is found PR model outperformed in ex-ante condition, while QPR model outperformed in ex-post condition. Further, QPR model performed consistently across validation and testing periods. Overall, QPR model excelled in capturing uncertainty in exogenous predictors, thereby reducing over-forecast error and risk of overinvestment.

Research limitations/implications

These findings can help utilities to align model selection strategies with their risk tolerance.

Originality/value

To propose the systematic model selection procedure in this study, the consistent performance of PR, GAM, QPR and QSR models are evaluated using point forecast accuracy metrics Mean Absolute Percentage Error, Root Mean Squared Error and probabilistic forecast accuracy metric Pinball Score for ex-ante and ex-post cases considering uncertainty in the considered exogenous predictors such as RGSP, RPCI, population and temperature.

Purpose

This study aims to analyze the existing body of knowledge concentrating on institutional investors’ behavior. It seeks to track how this domain has evolved through collaborative networks, as well as significant contributors, themes and research opportunities for future work.

Design/methodology/approach

The present study applies bibliometric analysis to examine the trends in the selected research field, using 446 articles from highly recognized journals indexed in the Scopus database.

Findings

The authors discovered that research on institutional investors’ behavior has significantly increased over the past four decades due to academic interest in the topic. This study observed five themes that unite the research in this field: institutional investors and corporate behavior; determinants of institutional investors’ trading patterns and performance; trading activity and its outcomes; herding, causes and consequences; and institutional investment and corporate performance. Moreover, future directions are penned down, such as how institutional investors’ control influences governance disclosures.

Originality/value

This study serves as a guide by mapping and analyzing the intellectual development of the research literature on institutional investors’ behavior. The authors contribute to the knowledge base by providing a solid foundation for further studies.

Purpose

The purpose of this article is to investigate and analyze the interactions between economic and sustainable development elements in the context of remanufacturing in India.

Design/methodology/approach

To comprehend the hierarchical and contextual link among factors impacting remanufacturing in India, the study used interpretive structural modeling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) methodologies.

Findings

The integrated ISM-DEMATEL approach identifies optimal utilization of the resources as the most crucial factor influencing remanufacturing in India, followed by reducing landfills, conserving energy and low cost. The study also reveals that optimal utilization of resources, reduction of landfills, conservation of energy and incorporated advanced technology impacts most of the factors but get affected by a few factors.

Practical implications

Industry practitioners and policymakers should consider the remanufacturing process to achieve sustainable and economic development. The government and other stakeholders can use the ISM framework and cause-and-effect diagram to classify the impact factors and their impact on the Indian economy and environment.

Social implications

This study supports the process to save the landfills and curbing pollution, conserve energy and optimize utilization of the resources, generate employment and supporting the development of the economy. Remanufacturing will undoubtedly contribute to the development of an environment and economy in India that benefits both.

Originality/value

ISM and DEMATELs strategy offers a tiered model and a cause-and-effect relationship between the variables affecting remanufacturing in India.

Purpose

This work presents the results of a case study aimed at revitalizing an agricultural equipment manufacturing consortium facing prolonged losses. The purpose of this paper is to enhance productivity and profitability by identifying and eliminating waste within the manufacturing processes. The study uses lean principles and tools to achieve this objective.

Design/methodology/approach

The study begins with the creation of a questionnaire, administered to the consortium to gather insights. The questionnaire responses serve as a foundation for pinpointing critical areas in need of immediate attention. To tackle the challenge of demand forecasting without customer data, a demand forecasting model is introduced. Value stream mapping (VSM) is used to identify and highlight process inefficiencies and waste. The findings are further analyzed using a Pareto chart to prioritize waste reduction efforts. Based on these insights, the study proposes alternative manufacturing methods and waste elimination strategies. A multiphase lean framework is developed as a step-by-step roadmap for implementing lean manufacturing.

Findings

The study identifies a broken process flow within the consortium’s manufacturing processes and highlights areas of waste through VSM. The Pareto chart analysis reveals the most significant waste areas requiring immediate intervention. Recommendations for process improvements and waste reduction strategies are provided to the consortium.

Originality/value

This study contributes to the field by applying lean principles and tools to address the unique challenges faced by an agricultural equipment manufacturing consortium. The integration of a demand forecasting model and the development of a multiphase lean framework offer innovative approaches to enhancing productivity and profitability in this context.

Purpose

This study aims to examine the role of various Instagram contents on customer behavior. The studied case is associated with herbal teas sold on active Instagram pages.

Design/methodology/approach

A structured questionnaire is exploited to collect data from customers. The integration of two entropy weight methods and weighted sum method are used to evaluate the priority of contents. In addition, model development is illustrated through which Instagram broadcasting tools are prioritized benefiting from fuzzy cognitive map method.

Findings

The results illustrate that customer behavior is moderated by content types, which empirically enhances the profitability of the business. The results of this study reveal that educational live, show other ones experience at live as well as motivational lives are the most effective contents. This study is a pioneering one to practically assess the construct of social media engagement through the effects of content types on the Instagram platform.

Originality/value

The effects of various contents, including motivational content, other customers’ experiences, products, educational content and purchase bill, on customer behavior are studied.

Purpose

This paper aims to explore the underlying intention behind using blockchain technology (BLCT) in the agri-food supply chain (AFSC). This is achieved by using a conceptual framework based on technology acceptance models that considers various factors influencing user behavior toward implementing this technology in their practices.

Design/methodology/approach

The conceptual framework developed is empirically validated using structural equation modeling (SEM). A total of 258 respondents from agri-food domain in India were involved in this survey, and their responses were analyzed through SEM to validate our conceptual framework.

Findings

The findings state that food safety and security, traceability, transparency and cost highly influence the intention to use BLCT. Decision-makers of the AFSCs are more inclined to embrace BLCT if they perceive the usefulness of the technology as valuable and believe it will enhance their productivity.

Practical implications

This study contributes to the existing literature by providing thorough examination of the variables that influence the intention to adopt BLCT within the AFSC. The insights aim to benefit industry decision-makers, supply chain practitioners and policymakers in their decision-making processes regarding BLCT adoption in the AFSC.

Originality/value

This study investigates how decision-makers’ perceptions of BLCT influence their intention to use it in AFSCs, as well as the impact of the different underlying factors deemed valuable in the adoption process of this technology.

Purpose

This study aims to evaluate the impact of sustainable strategic planning and management (SSPM) on sustainable performance (ecological, social and economic) among micro, small and medium enterprises (MSME) in the culinary sector in Southeast Asia (SEA).

Design/methodology/approach

This study used a quantitative approach surveying 300 halal-certified MSMS culinary businesses in the SEA region. Data were gathered using an online survey platform and analyzed using the partial least squares structural equation modeling technique.

Findings

Green management (GRM), digital transformation management (DTM) and Halal Business Management (HBM) exert a significant influence on SSPM. Furthermore, SSPM positively influences the sampled halal culinary MSMEs’ ecological, social and economic performances in SEA. The study also reveals that GRM, DTM and HBM positively influence Islamic ethics compliance and good governance.

Research limitations/implications

This study sheds light on the often-ignored MSME business in the halal culinary sector in SEA. Examining sustainable and strategic planning synergy strengthens the development of a comprehensive framework for SSPM and could encourage more MSMEs to practice sustainable business operations. Moreover, the research also underlines the urgent need to harmonize halal standards in the SEA region. The approach would intensify multilateral support for economic growth and halal trade between SEA countries and beyond.

Originality/value

This study addresses the research gap in the strategic and sustainable management of halal culinary MSMEs within the developing and vast SEA economic region. This research is among the few, if not the first, that combines the GRM, DTM and HBM influence on SSPM and examines the impact on sustainable, ethical and good governance performances. The insights derived from this study contribute to formulating robust, sustainable policies, fostering avenues for sustainable initiatives and enhancing regional and global cooperation for a sustainable future.