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Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

This study analyzes the performance implications of adopting blockchain to support supply chain business processes. The technology holds as many promises as implementation challenges, so interest in its impact on operational performance has grown steadily over the last few years.

Drawing on transaction cost economics and the contingency theory, we built a set of hypotheses. These were tested through a long-term event study and an ordinary least squares regression involving 130 adopters listed in North America.

Compared with the control sample, adopters displayed significant abnormal performance in terms of labor productivity, operating cycle and profitability, whereas sales appeared unaffected. Firms in regulated settings and closer to the end customer showed more positive effects. Neither industry-level competition nor the early involvement of a project partner emerged as relevant contextual factors.

This research presents the first extensive analysis of operational performance based on objective measures. In contrast to previous studies and theoretical predictions, the results indicate that blockchain adoption is not associated with sales improvement. This can be explained considering that secure data storage and sharing do not guarantee the factual credibility of recorded data, which needs to be proved to customers in alternative ways. Conversely, improvements in other operational performance dimensions confirm that blockchain can support inter-organizational transactions more efficiently. The results are relevant in times when, following hype, there are signs of disengagement with the technology.

Pre-pandemic research demonstrated the challenges of the nursing workforce and the provision of quality of patient care. Such challenges have been significantly intensified during the COVID-19 pandemic, not least in the workplace and fear of staff catching and transmitting COVID-19. We draw on conservation of resources (COR) theory to examine the impact of the fear of COVID-19 on nurses and the role of well-being-HRM (WBHRM) in negating the fear of COVID-19 and its impact on job stress and perceived quality of patient care.

We collected data from 260 nurses (treating COVID-19 patients) employed in US hospitals across two-waves. Data were analyzed using mediated regression and moderated mediation.

The results indicated that when nurses report higher levels of fear of COVID-19, this translates into higher levels of nursing job stress. This, in turn, reduces nurses’ perceptions of quality of patient care they can provide. As previous research has found, decreased perceptions of quality of patient care is a significant factor driving intentions to leave the profession. The results demonstrated that WBHRM practices buffer the negative impact of fear of COVID-19 on job stress, and in turn, the perceived quality of patient care.

Our paper contributes to new knowledge for healthcare managers on WBHRM bundles and their efficacy in buffering the effects of fear on job stress and quality of patient care. We contribute new knowledge on fear at work and how to manage employees’ fear through WBHRM practices.

As the risk of uncertain quality of used products potentially hinders remanufacturing, this study aims to examine the impact of risk aversion under quality uncertainty of used products in a remanufacturing supply chain (RSC) consisting of a manufacturer and an independent remanufacturer.

We develop an RSC model where the manufacturer produces new products, outsources remanufacturing to the independent remanufacturer and sells both new and remanufactured products to end consumers. Using a manufacturer-led Stackelberg game framework, we derive the equilibrium solutions under risk-neutral and risk-averse scenarios. Additionally, we design a two-part tariff contract to achieve coordination.

We show that while risk aversion leads the manufacturer to raise the outsourcing fee, which in turn reduces both the remanufactured quantity and the collection rate of used products. Consequently, consumer surplus and social welfare decline, while environmental impacts rise. The proposed two-part tariff contract can improve the collection rate and social welfare. We also explore two extensions: an authorization remanufacturing scenario and a two-period scenario. We find that risk aversion has no impact on the selection of remanufacturing mode and the equilibria in the first period. Our findings provide timely managerial insights for RSC management.

One of the main risks deterring remanufacturing is the quality uncertainty of used products. However, the risk aversion arising from this uncertainty and its effects have rarely been studied within a game-theoretic framework. This paper fills this gap by analyzing the remanufacturer’s risk aversion under quality uncertainty and investigating its impacts.

This study investigates the variables that impact initial public offerings (IPO) initial returns witnessed in Pakistan from 1996 to 2019 using pre-listing information variables, namely country-level institutional quality, sponsor ownership and pricing mechanism. IPO oversubscription is included as a moderating variable.

This research is motivated by the premise that the Pakistani IPO market is characterised by a broad range of institutional and regulatory frameworks. Multiple regression studies, i.e. ordinary least square (OLS) and quantile least square (QLS), were performed on 102 IPOs issued on the Pakistan stock market.

The present study findings suggest that the quality of public service, the independence of civil service from political influences and the legitimacy of government increase investors’ confidence in the prospects of companies, hence increasing the demand for IPO and initial returns. In addition, good regulatory quality enhances market transparency and lowers uncertainty, hence signalling high-quality IPOs and leading in substantial initial returns. The negative effect of the lock-up ratio on the initial return of an IPO is consistent with the risk-return trade-off theory, which asserts that the lock-up ratio indicates the quality of the IPO.

The results provide market regulators, policymakers, investors and underwriters with useful data for assuring proper subscriptions of issued shares, as these variables are crucial for company transparency and market efficiency. The findings will also help investors make better IPO subscription decisions.

The present study explains the important influencing factors of IPO initial return in the Pakistani market.

This study aims to investigate the influence of the reviewed establishment’s price level and the user’s social network size and reputation status on consumers’ tendency to post more visual imagery content. Furthermore, it explores the moderating effects of user experiences and geographic distance on these dynamics.

This study adopts a multi-method approach to explore both the determinants behind the sharing of user-generated photos in online reviews and their internal mechanisms. Using a comprehensive secondary data set from Yelp.com, the authors focused on restaurant reviews from a prominent tourist destination to construct econometric models incorporating time-fixed effects. To enhance the robustness of the authors’ findings, the authors complemented the big data analysis with a series of controlled experiments.

The reviewed establishments price level and the users reputation status and social network size incite corresponding motivations conspicuous display “reputation seeking” and social approval motivating users to incorporate more images in reviews. “User experiences can amplify the influence of these factors on image sharing.” An increase in the users geographical distance lessens the impact of the price level on image sharing, but it heightens the influence of the users reputation and social network size on the number of shared images.

As a result of this study, high-end establishments can increase their online visibility by leveraging user-generated visual content. A structured rewards program could significantly boost engagement by incentivizing photo sharing, particularly among users with elite status and extensive social networks. Additionally, online review platforms can enhance users’ experiences and foster more dynamic interactions by developing personalized features that encourage visual content production.

This research, anchored in trait activation theory, offers an innovative examination of the determinants of photo-posting behavior in online reviews by enriching the understanding of how the intricate interplay between users’ characteristics and situational cues can shape online review practices.

This study develops a novel method for mitigating credit risk through the use of structured derivatives, focusing in particular on the use of European put options as a strategic hedging tool. Inspired by the work of Merton (1974), our approach introduces the concept of default triggered by the stock price S_T breaching a predefined barrier B. By establishing a distributional equivalence between an existing default model and P(ST<B) for a given time T, we demonstrate the potential for reducing the necessary capital allocation for a projected loss X(T) by partially hedging with a European put option. We formulate and solve an optimization problem w.r.t. a specific risk measure to determine the optimal strike price for the option, and our numerical analysis confirms a reduction in the Solvency Capital Requirement (SCR) in markets with and without jumps. Our findings provide (insurance) companies with a pragmatic approach to mitigating losses while maintaining their current risk management framework.

In the era of the circular economy, the economic growth of a country is highly dependent on the sustainable performance of the manufacturing sector. In today’s increasingly competitive world, it is important to constantly improve the manufacturing or service industry. Quality with quantity is a main characteristic, which helps a company stay in the competition. Flexibility and responsiveness to customer demands are very important for success. Generally, additional time is needed for setup caused by poor design of equipment. At this point, the terms continuous process improvement and single-minute exchange of dies (SMED) as an approach of lean manufacturing come into play. Lean manufacturing system has been identified as an approach for improving the performance of the process and product.

In this paper, high setup time is considered as major problem in the industry and a major cause and effect for high setup time was found. On the basis of the literature review and experts’ opinions, four categories of barriers, namely method, manpower, machine and tools are identified. In this study, a hybrid approach comprising of the analytic hierarchy process (AHP) and graph theoretic approach (GTA) has been used. First, prioritization of different categories of barriers by AHP has been done, and second, GTA has been applied for finding the barriers' intensity index. Based on this study, machine and manpower barriers have emerged as major hurdles in the high setup time of machine. The proposed framework will help organizations quantify barriers in high setup time in different manufacturing processes, thereby developing effective strategies for sustainable production.

Findings of this research will contribute to ensuring sustainable competitive advantages, but it has some limitations. Development of the permanent matrix equation for barriers of high setup time is complex and lengthy when barriers are more in number. Absolute and relative values considered while quantifying the intensity of barriers are based on experts’ opinions, which may be inconsistent. In spite of these limitations, organizations can use an illustrated approach to quantify the barriers, thereby developing strategies for successful implementation of high setup time for making them sustainable in the global market. Organizations can also benchmark their sustainability preparedness with the best in the industry. As a future scope of study, high setup time can be reduced by using SMDE technology can be further validated through an empirical and case-based approach to generalize the findings.

Authors remain confined only to setup time. The approach is generalizable and can be extended in other areas. As a future scope of study, high setup time can be reduced by using single minute die exchange technology and can be further validated through an empirical and case-based approach to generalize the findings.

The study guides and facilitates researchers and practitioners in using the most appropriate techniques such as AHP and GTA for empirical studies and in developing, modifying and/or reviewing application frameworks for production. It also guides implementation experience regarding high setup time by using advanced techniques such as single point die exchange (SMDE), which can be beneficiary for both developing and developed country contexts. Industries can accelerate implementation by understanding and using most important AHP, GTA and SMDE techniques.

Lean manufacturing system has been identified as an approach for improving the performance of the process and product. A lean manufacturing system is part of corporate culture, like tools and approaches. High setup time can be classified as waste for the company. Reduction in time is a direct way to increase the productivity and profit. Therefore, there is a need to reduce the time by using some new lean methodology. In global industry, different techniques are used for reduction of time.

The problem of food security depends highly on food availability along with access, utilization and stability. In this study, we would like to focus on the food availability criterion. With the rise in food prices, production of food is expected to increase in the domestic as well as in the international market. Therefore, the G-20 countries collectively, which produce up to 80% of the world's cereals and also have the largest percentage share of world agricultural exports, ought to benefit from the rise in agricultural prices. However, a significant number of countries in this group are net importers in terms of agricultural inputs. Also, the rise in the prices of non-renewable resources raises the demand and cost of biofuels. Commodity trading and the weakening of the exchange rate also affect farm prices. A rise in farm prices from these accounts may not be supportive of to rise in food production and food security. Hence, it will be interesting to analyze the effect of rising food prices on the problem of food security of the G-20 countries from the perspective of food production. In this study, we have taken rice as a representative food item and found that rice production is significantly responsive to the domestic relative price of rice. However, there is no price incentive operative for rice productivity. Rather, we find evidence of a negative relationship between rice productivity and domestic rice prices in relation to the international prices.

The purpose of this study is to investigate the drivers behind the notable increase in red meat prices in Turkey, attributing the rise primarily to policy interventions within the dairy industry which result in a reduction of farm capacity for dairy farmers.

To analyze the factors that determine the price of carcass red meat, cattle fattening feed is identified as a variable that directly affects carcass pricing, while the ratio of farm gate milk prices to milk feed prices is assumed to have an indirect impact. Monthly data from January 2011 to December 2022 are used in the analysis. The time series examined is influenced by time-dependent nonlinear Fourier trends that are computed for use in the estimation part.

The study reveals that fluctuations in cattle fattening feed prices have an immediate impact on carcass prices, while changes in the ratio of raw milk prices to milk feed prices exhibit a time lag. Although short-term interventions to the farm gate milk prices can initially suppress carcass prices, it is important to consider their medium and long-term effects, as these can manifest as a decrease in the number of animals available.

The research sheds light on the intricate interplay between policy interventions in the dairy sector and their ripple effects on the broader red meat market, contributing valuable insights to both academic and policy discourse.