This paper presents a solution to the problem of ranking efficient decision-making units (DMUs) in data envelopment analysis (DEA). We develop a cross-inefficiency approach for the deviation variables framework based on a pair of epsilon-based benevolent and aggressive models for both constant and variable returns-to-scale technologies. The new method improves the discriminating power of DEA, solves the non-uniqueness of ranking solutions, and avoids the negative efficiency scores associated with current models in the deviation variables framework. We illustrate the performance of the approach using a real-life case study. Not only does the research improve the discriminating power, but it also encourages the first step towards integrating the deviation variables framework in the context of decision-making uncertainty.

We propose a dynamic ensemble selection method, META-DES-AAP, to predict the success of bank telemarketing sales of time deposits. Unlike existing machine learning-based marketing sales prediction methods focusing only on prediction accuracy, META-DES-AAP considers the accuracy and average profit maximization. In META-DES-AAP, to consider both accuracy and average profit in the framework of dynamic ensemble selection using meta-training, a multi-objective optimization algorithm is designed to maximize the accuracy and average profit for base classifiers selection. Base classifiers suitable for each test telemarketing campaign are integrated according to the dynamic-based base classifiers integration method. Experimental results on bank telemarketing data show that META-DES-AAP achieves the best accuracy and the largest average profit when compared across several state-of-the-art machine learning methods. In addition, the factors influencing telemarketing on the average predicted probability of telemarketing success and average profit obtained by META-DES-AAP are analyzed.

One of the significant challenges in the sports industry is identifying the factors influencing match results and their respective weightage. For appropriate recommendations to the team management and the team players, there is a need to predict the match and quantify the important factors for which prediction models need to be developed. The second thing required is identifying talented and emerging players and performing an associative analysis of the important factors to the match-winning outcome. This paper formulates a hybrid machine learning-clustering-associative rules model. This paper also implements the framework for cricket matches, one of the most popular sports globally watched by billions around the world. We predict the match outcome for One day Internationals (ODIs) and Twenty 20 s (T20s) (two formats of Cricket representing fifty over and twenty over versions respectively) adopting state-of-the-art machine learning algorithms, Random Forest, Gradient Boosting, and Deep neural networks. The variable importance is computed using machine-learning techniques and further statistically validated through the regression model. The emerging talented players are identified by clustering. Association rules are generated for determining the best possible winning outcome. The results show that environmental conditions are equally crucial for determining a match result, as are internal quantitative factors. The model is thus helpful for both team management and for players to improve their winning strategy and also for discovering emerging players to form an unbeatable team.

The transparency of online reviews of drug treatment in patients with diabetes supports the use of text analytics to investigate review helpfulness based on the dual-process theory and design science approach. The first purpose of our study is to explore the influences of informational elements (emotions with the degrees of different arousal, review length) and normative elements (perceived effectiveness and ease of use, and patient satisfaction) in online drug treatment reviews on review helpfulness. We also examine the moderate role of review length on the relationship between patient satisfaction and review helpfulness. The second purpose is to explore the influences of the review topics on review helpfulness. Our study reveals four essential findings. First, not all emotions significantly influence review helpfulness, and only low-arousal emotions have a significant positive influence on review helpfulness. Second, an inverted U-shaped relationship between review length and review helpfulness and a U-shaped relationship between patient satisfaction and review helpfulness are confirmed. Third, review length has a moderate influence on the inverted U-shaped relationship between patient satisfaction and review helpfulness. Finally, the review topics related to blood sugar, family medical history, dosing time and injection significantly influence review helpfulness. These findings may serve as a stepping stone for future research on review helpfulness in the healthcare context, offering guidance for patients with diabetes, design implications for platform providers, and drug improvement suggestions for pharmaceutical companies.

The COVID-19 pandemic has had a severe impact on mankind, causing physical suffering and deaths across the globe. Even those who have not contracted the virus have experienced its far-reaching impacts, particularly on their mental health. The increased incidences of psychological problems, anxiety associated with the infection, social restrictions, economic downturn, etc. are likely to aggravate with the virus spread and leave a longer impact on humankind. These reasons in aggregation have raised concerns on mental health and created a need to identify novel precursors of depression and suicidal tendencies during COVID-19. Identifying factors affecting mental health and causing suicidal ideation is of paramount importance for timely intervention and suicide prevention. This study, thus, bridges this gap by utilizing computational intelligence and Natural Language Processing (NLP) to unveil the factors underlying mental health issues. We observed that the pandemic and subsequent lockdown anxiety emerged as significant factors leading to poor mental health outcomes after the onset of COVID-19. Consistent with previous works, we found that psychological disorders have remained pre-eminent. Interestingly, financial burden was found to cause suicidal ideation before the pandemic, while it led to higher odds of depressive (non-suicidal) thoughts for individuals who lost their jobs. This study offers significant implications for health policy makers, governments, psychiatric practitioners, and psychologists.

This paper studies the resilience of logistics network against node failures in the context of express industry owing to disruption in the network. By considering the flow capacity between the nodes and the impact of each node's failure, we propose a load redistribution mechanism in the presence of cascading failures which is akin to a criticality-based resilience assessment or stress testing the supply chain. To further investigate the impact of the node/nodes failure, we simulate and propose algorithms for two cascading failure scenarios, illustrating the different adjustment schemes for resilience improving strategies. A sensitivity analysis with managerial insights is also performed to investigate the effect of the adjustment schemes on the criticality of the nodes and the resilience of the express logistics network.

Eco-innovation (EI) allows organizations to achieve environmental and economic gains but the conditions for successful EI to occur are unclear. This paper proposes that firms can improve their EI performance by adopting circular economy (CE) systems and technologies, such as product take-back and recycling. We thus explore an unconventional, systems- and technology-driven path dependence dynamic leading to higher EI performance because of prior CE commitments (instead of managerial initiatives directly aimed at fostering EI). An analysis of 724 firms shows that the adoption of such technologies and systems puts firms on a sustainability path: firms benefit from making further changes to improve the functioning of these systems, which in turn create the conditions and capabilities for future EIs. Environmental supply chain policies thus mediate the relationship as the firm adopting take-back and recycling needs to involve outside partners in their administration. Neither environmental management systems nor data protection policies act as mediators. This is the first quantitative study that considers take-back and recycling and EI scores with a cross-national-cross-sectorial sample size. The results indicate that firms should see the introduction of CE systems and technologies as an opportunity for EI.

Credit score is a creditworthiness index, which enables the lender (bank and credit card companies) to evaluate its own risk exposure toward a particular potential customer. There are several credit scoring methods available in the literature, but one that is widely used is the FICO method. This method provides a score ranging from 300 to 850 as a fast filter for high-volume complex credit decisions. However, it falls short in the aspect of a decision support system where revised scoring can be achieved to reflect the borrower’s strength and weakness in each scoring dimension, as well as the possible trade-offs made to maintain one’s lending risk. Hence, this study discusses and develops a decision support tool for credit score model based on multi-criteria decision-making principles. In the proposed methodology, criteria weights are generated by fuzzy AHP. Fuzzy linguistic theory is applied in AHP to describe the uncertainties and vagueness arising from human subjectivity in decision making. Finally, drawing from the risk distance function, TOPSIS is used to rank the alternatives based on the least risk exposure. A sensitivity analysis is also demonstrated by the proposed fuzzy AHP-TOPSIS method.

The major challenges of deregulation are lax market entry, sudden surge in new market entrants, and the intense price wars that ensue, thus causing major losses for any industry. This paper investigates whether deregulation can be structured through a controlled Mergers and Acquisitions (M&As) process by means of government intervention, and how this promotes the performance of the players in the industry. We study this in the context of the Chinese aviation industry as an ideal microcosm of our problem statement. This is because China’s civil aviation industry has witnessed many of the above challenges since its deregulation and economic reforms in 1979, which saw the beginning of a transformation from a fully state-owned machinery to a rent-seeking private sector. The post controlled deregulation process through M&As led to three dominant carriers: Air China Limited (AC), China Southern Airlines (CS), and China Eastern Airlines (CE). Using a 3-player non-cooperative perfect information Cournot oligopoly game model, the strategic efficacy of the government intervention to consolidate the industry based on operating expenses, air passenger revenue, and profit data are investigated respectively. All three airlines are better off after the exercise, with the industry facing a more sustainable growth by the intervention.

The busiest container ports in the world are mostly situated in the Asian region, thus intensifying inter-port competition. Particularly unique in this region, is the shared history between Malaysia and Singapore, which was once ruled-under the British empire, has since become economic rivals for the Far East – Europe trade route. This provides a suitable context to investigate whether competition or a strategic alliance should be adopted for ports in terms of its benefit to the industry in the region as a whole. Specifically, this paper analyses the ocean freight traffic demand for the Far East-Europe route among three main transshipment ports located in Malaysia and Singapore: Port of Singapore (PSA), Port Klang (PKL), and Port of Tanjung Pelepas (PTP). The paper provides a scenario analysis of the 3-way interaction through a game theoretic model. The results suggest that a strategic alliance between PSA and PTP generates greater profitability to the current hub and spoke network, while PKL should not commit to any cooperative strategy with either PSA or PTP.

This paper employs a novel approach in modeling exchange rates based on the recent developments in wavelet analysis. Maximum overlap discrete wavelet transform (MODWT) was used to decompose exchange rate and interest rate differentials into several scales based on time and frequency, while the causal nexus was investigated via nonlinear Granger causality. To illustrate the practicality of wavelet methodology, we carried out an empirical application in China relative to the U.S using monthly data. The results prove the relevance of wavelet methodology in deciphering a meaningful time-scale relationship that challenges prior evidence found in the literature when employing conventional econometric methods.