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1 – 10 of 145Gianluca Elia, Gianpaolo Ghiani, Emanuele Manni and Alessandro Margherita
This study aims to present a methodology and a system to support the technical and managerial issues involved in anomaly detection within the reverse logistics process of an…
Abstract
Purpose
This study aims to present a methodology and a system to support the technical and managerial issues involved in anomaly detection within the reverse logistics process of an e-commerce company.
Design/methodology/approach
A case study approach is used to document the company’s experience, with interviews of key stakeholders and integration of obtained evidence with secondary data.
Findings
The paper presents an algorithm and a system to support a more efficient and smart management of reverse logistics based on a set of anticipatory actions, and continuous and automatic monitoring of returned goods. Improvements are described in terms of a number of key performance indicators.
Research limitations/implications
The analysis and the developed system need further applications and validations in other organizational contexts. However, the research presents a roadmap and a research agenda for the reverse logistics transformation in Industry 4.0, by also providing new insights to design a multidimensional performance dashboard for reverse logistics.
Practical implications
The paper describes a replicable experience and provides checklists for implementing similar initiatives in the domain of reverse logistics, in the aim to increase the company’s performance along four key complementary dimensions, i.e. time savings, accuracy, completeness of data analysis and interpretation and cost efficiency.
Originality/value
The main novelty of the study stays in carrying out a classification of anomalies by type and product category, with related causes, and in proposing operational recommendations, including process monitoring and control indicators that can be included to design a reverse logistics performance dashboard.
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Non-profit organizations (NPOs) are exposed to a highly competitive environment in which they are forced to grow their commercial activity to acquire additional financial…
Abstract
Purpose
Non-profit organizations (NPOs) are exposed to a highly competitive environment in which they are forced to grow their commercial activity to acquire additional financial resources. This study aims to create an understanding of how NPOs involved in textile reuse as a revenue-generating programme manage their reverse supply chains (RSC).
Design/methodology/approach
The research involves an embedded single-case study of NPOs in Finland involved in post-use textile collection. The main data sources are semi-structured interviews and participant observations.
Findings
This study is inspired by the microfoundations movement and identifies the underlying microfoundations of the NPOs’ capabilities for managing RSC for textile reuse. The study contributes to the literature by demonstrating NPOs’ lower-level, granular practices and their adaptations for achieving quality outcomes in textile reuse.
Research limitations/implications
The findings have context sensitivity and apply to the NPOs which operate in a context similar to Finland, such as in other Nordic countries.
Practical implications
This study continues the discussion on the adoption of “business-like” practices in the NPOs’ pursuit of additional revenue streams to finance humanitarian work. The findings of this study can also be transferred to the growing area of domestic textile circularity.
Social implications
Using the case of NPOs in textile reuse, the study illustrates how RSC management can serve a social, non-profit cause and transform unwanted textile products into a source of fundraising for humanitarian work.
Originality/value
This enriches the understanding of NPOs’ practices within the scope of revenue-generating programmes by examining one of them – textile reuse through charity shops from an RSC perspective.
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Social media marketing has become a powerful strategic tool for many brands, but scholarly research in this domain is still in its infancy. This study aims to examine the effects…
Abstract
Purpose
Social media marketing has become a powerful strategic tool for many brands, but scholarly research in this domain is still in its infancy. This study aims to examine the effects of social media marketing activities on consumer online impulse buying intentions via brand resonance and emotional responses by incorporating the direct and moderating effects of social network proneness toward fashion retail brands.
Design/methodology/approach
By using snowball sampling, this study recruited 441 netizens (who were using fashion retail brands) and obtained their responses through an online survey. Structural equation modeling was applied to 394 responses for analysis.
Findings
The findings discovered that social media marketing activities significantly influenced brand resonance, consumer emotional responses and online impulse buying intentions. Likewise, brand resonance and emotional responses were positively associated with online impulse buying intentions and acted as decisive mediators. Social network proneness’s direct and moderating effects significantly increased consumer online impulse-buying intentions toward fashion retail brands.
Practical implications
This study provides recommendations to retail managers for creating and executing brand positioning, segmenting and targeting strategies to enhance consumers’ intentions for engaging in online impulsive purchases for fashion brands.
Originality/value
This original research contributes to the branding literature and stimulus–organism–response theory by focusing on social media marketing activities, brand resonance, emotional responses, social network proneness and consumer online impulse buying intentions toward fashion retail brands.
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La Ode Nazaruddin, Md Tota Miah, Aries Susanty, Maria Fekete-Farkas, Zsuzsanna Naárné Tóth and Gyenge Balázs
This study aims to uncover apple preference and consumption in Indonesia, to disclose the risk of non-halal contamination of apples and the importance of maintaining the halal…
Abstract
Purpose
This study aims to uncover apple preference and consumption in Indonesia, to disclose the risk of non-halal contamination of apples and the importance of maintaining the halal integrity of apples along the supply chain and to uncover the impacts of food miles of apples along supply chain segmentation.
Design/methodology/approach
This study adopted mixed research methods under a fully mixed sequential dominant status design (QUAN → qual). Data were collected through a survey in some Indonesian provinces (N = 396 respondents). Samples were collected randomly from individual consumers. The qualitative data were collected through interviews with 15 apple traders in Indonesia. Data were analysed using crosstab, chi-square and descriptive analysis.
Findings
First, Muslim consumers believe in the risk of chemical treatment of apples because it can affect the halal status of apples. Second, Indonesian consumers consider the importance of halal certification of chemical-treated apples and the additives for apple treatments. Third, the insignificance of domestic apple preference contributes to longer food miles at the first- and middle-mile stages (preference for imported apples). Fourth, apple consumption and shopping distance contribute to the longer food miles problem at the last-mile stage. Fifth, longer food miles have negative impacts, such as emissions and pollution, food loss and waste, food insecurity, financial loss, slow development of the local economy and food unsafety.
Practical implications
This research has implications for the governments, farmers, consumers (society) and business sectors.
Originality/value
This study proposes a framework of food miles under a halal supply chain (halal food miles) to reduce the risk of food miles and improve halal integrity. The findings from this research have theoretical implications for the development of the food mile theory, halal food supply chain and green supply chain.
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Anwar Sadat Shimul, Anisur R. Faroque and Isaac Cheah
This research aims to examine the role of consumers' brand trust and attachment on advocacy intention before and after the occurrence of brand misconduct in retail banking. In…
Abstract
Purpose
This research aims to examine the role of consumers' brand trust and attachment on advocacy intention before and after the occurrence of brand misconduct in retail banking. In addition, the influence of brand attachment on consumers' willingness to switch, advocate for and forgive brands is examined in a post-misconduct scenario.
Design/methodology/approach
Data were collected through a self-administered online survey questionnaire. A total of 304 valid and usable responses from Australian participants were analysed using IBM SPSS 27.0.
Findings
The findings reveal that brand attachment mediates the positive relationship between trust and advocacy intention. Furthermore, brand attachment (1) dilutes consumers' switching intention and (2) strengthens their willingness to forgive the bank after misconduct.
Practical implications
Results suggest that retail banks should create strong brand attachments with their consumers. In addition to brand trust, brand attachment will generate greater advocacy intention among consumers. Moreover, practitioners in retail banking can leverage brand attachment to mitigate the negative impact of brand misconduct.
Originality/value
To the best of the authors' knowledge, this study is the first to examine the impact of brand attachment on the consumer–bank relationship within the context of brand misconduct. The study is also unique in its analysis of the mediating role of brand attachment between brand trust and advocacy. This research further adds to the current literature by suggesting that strong and positive customer connections to the brand facilitate communication and marketing efforts after brand misconduct and that these are effective in maintaining consumer-bank relationship.
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Çağla Cergibozan and İlker Gölcük
The study aims to propose a decision-support system to determine the location of a regional disaster logistics warehouse. Emphasizing the importance of disaster logistics, it…
Abstract
Purpose
The study aims to propose a decision-support system to determine the location of a regional disaster logistics warehouse. Emphasizing the importance of disaster logistics, it considers the criteria to be evaluated for warehouse location selection. It is aimed to determine a warehouse location that will serve the disaster victims most efficiently in case of a disaster by making an application for the province of Izmir, where a massive earthquake hit in 2020.
Design/methodology/approach
The paper proposes a fuzzy best–worst method to evaluate the alternative locations for the warehouse. The method considers the linguistic evaluations of the decision-makers and provides an advantage in terms of comparison consistency. The alternatives were identified through interviews and discussions with a group of experts in the fields of humanitarian aid and disaster relief operations. The group consists of academics and a vice-governor, who had worked in Izmir. The results of a previously conducted questionnaire were also used in determining these locations.
Findings
It is shown how the method will be applied to this problem, and the most effective location for the disaster logistics warehouse in Izmir has been determined.
Originality/value
This study contributes to disaster preparedness and brings a solution to the organization of the logistics services in Izmir.
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Md Rakibul Hasan, Yosef Daryanto, Chefi Triki and Adel Elomri
The rapidly growing e-commerce industry with its special characteristics brings new challenges to the optimization of the supply chain and inventory management. This study aims to…
Abstract
Purpose
The rapidly growing e-commerce industry with its special characteristics brings new challenges to the optimization of the supply chain and inventory management. This study aims to investigate the inventory-related optimization of an e-marketplace official store that works on a business-to-customer system when cashback promotion is used to attract more customers. Also, it proposes a new inventory model to maximize the e-commerce profit by optimizing the cashback amount and delivery period.
Design/methodology/approach
The proposed model assumes that customer demand is a function of price and delivery time and that price is affected by the cashback amount. The e-commerce operator has a profit-sharing contract with an e-payment company that facilitates the payment. E-commerce also builds collaboration under a cost-sharing contract with a supplier to ensure product delivery. A mathematical model is developed and the related theories are investigated. A numerical example illustrates the validity of the model and a sensitivity analysis is carried out to give useful insights.
Findings
A new inventory model for an e-market system has been introduced which shows the impact of a cashback promotion on the e-commerce business. This study shows that managers can optimize the cashback amount and its delivery time to get the maximum profit. In certain cases, the manager may set a high cashback amount (e.g. 100%) to attract customers to place more orders.
Originality/value
This study presents a new inventory model for today’s fast-growing e-commerce business; therefore, the results contribute to the understanding of promotion program practices and inventory management and provide insights to develop efficient e-commerce managerial decisions.
Graphical abstract
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Muhammad Mohsin Khalil and Waqar Ahmed
In recent years, technological advancement has played a crucial role in the growth of emerging economies. However, as with any novel technological development, there are often…
Abstract
Purpose
In recent years, technological advancement has played a crucial role in the growth of emerging economies. However, as with any novel technological development, there are often concerns and hesitations surrounding its implementation. This study aims to investigate the factors influencing blockchain adoption and usage. Thereby evaluating its impact on supply chain performance.
Design/methodology/approach
This is a deductive research based on the modified form unified theory of acceptance and use of technology, which is a persuasive model that has been used in numerous studies on the acceptance and usage of information technology systems. For this study, valid data was collected from 129 management-level supply chain professionals and policymakers working in diverse manufacturing industries. The collected data was used for testing hypotheses by deploying the structural equation modeling technique.
Findings
The findings of this study reveal that facilitating conditions and technology readiness highly are key influencers for organizations to implement this disruptive technology. Moreover, blockchain adoption and usage can significantly enhance supply chain performance.
Originality/value
Blockchain technology is a novel and promising disruptive technology that industries are looking forward to adopting and using. For the policymakers and supply chain strategists working in a developing country, this study offers a comprehensive viewpoint on the swift acceptance and usage of blockchain technology to facilitate supply chain operations.
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Nicola Castellano, Roberto Del Gobbo and Lorenzo Leto
The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on…
Abstract
Purpose
The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.
Design/methodology/approach
The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.
Findings
The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.
Practical implications
The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.
Originality/value
This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.
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Travis Fried, Anne Victoria Goodchild, Ivan Sanchez-Diaz and Michael Browne
Despite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an…
Abstract
Purpose
Despite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.
Design/methodology/approach
The model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).
Findings
Evaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.
Originality/value
Presents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.
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