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The purpose of this paper is to evaluate the performance of Microfinance Institutions (MFIs) offering FinTech services. This study contributes to the existing literature on microfinance digitalization, financial inclusion and sustainable development. The study also takes into consideration a behavioral perspective through the efficiency evaluation process of MFIs offering FinTech services.

The following study employs the Stochastic Frontier Analysis approach to estimate the operational and social efficiency scores of the 387 MFIs over the period 2005–2019. Then, it tries to consider factors influencing MFIs' efficiency and assess their effects. Hence, two separate models for operation and social efficiency introducing a set of factors, including FinTech proxies and overconfidence proxies, are tested. The first model for operational efficiency uses a random-effects estimator while the second one for social efficiency uses a fixed-effects estimator.

The results show that innovative MFIs have weaker averages of operational efficiency than non-innovative ones but higher averages of social efficiency. This was justified by the fact that innovative MFIs are more socially oriented. Further, findings of this study depict that the proxies of FinTech affect negatively the level of operational efficiency of MFIs. They also depict a negative relationship between FinTech proxies and the level of social efficiency. These results hold through robustness tests.

The highlight of this study is that it takes heed of the indirect effect of technological innovation on the efficiency of MFIs. It has been proved that it moderates the impact of managerial overconfidence (manifested by excessive risk-taking, viz., high levels of PAR30, LGR and NIM) on the level of both operational and social efficiencies.

本文旨在對提供金融科技服務的微型金融機構的表現作出評價。我們的研究, 就現有之學術文獻而言, 在以下課題之探討上作出了貢獻: 微型金融的數字化、普惠金融、以及可持續發展。本研究亦以行為主義觀點, 對微型金融機構提供之金融科技服務的效率作出評價。

本研究使用隨機邊界分析法的理念, 去估計有關的387間微型金融機構於2005年至2019年期間、經營方面和社會方面的效率分數; 繼而嘗試找出影響微型金融機構效率的因素, 並評估這些因素的影響。為此目的, 研究人員分別測試兩個模型, 一個是探究運作方面的效率, 另一個則探究社會方面的效率。兩個模型內均放入一系列的因素, 其中包括金融科技代理和過度自信代理。探究運作方面的效率的模型使用了隨機效果估算器, 而探究社會方面的效率的模型則使用了固定效果估算器。

研究結果顯示、具創新精神的微型金融機構, 在運作方面的效率的平均值上,較沒具創新精神的為弱, 而社會方面的效率的平均值卻較高。這個結果是合理的, 因為具創新精神的微型金融機構會更著眼於社會。另外, 研究結果描繪了一個現象, 就是: 金融科技代理會對微型金融機構的運作效率水平產生負面影響; 我們也看到、金融科技代理與社會方面的效率水平之間的關聯是負面的; 這些研究結果、均通過穩健性檢驗。

本研究最突出之處為研究人員關注科技之創新會間接影響微型金融機構的效率。研究人員證明了於微型金融機構整合金融科技服務是會緩和管理上的過度自信給運作和社會兩方面的效率水平帶來的影響 (管理上的過度自信、顯露於過度的風險承擔, 即是, PAR30(貸款組合風險-30日)、LGR(貸款增長率) 和NIM(淨息差) 處於高水平)。

This study aims to provide a cutting-edge evaluation of the sharing economy's impact within the realm of tourism and hospitality. The primary objectives guiding this research are as follows: to uncover the prevalent discussions and debates within the tourism and hospitality sector concerning the implications and effects of the sharing economy on urban destinations; and to analyse how scholarly inquiries and empirical investigations have contributed to a comprehensive comprehension of the intricate theoretical foundations and practical intricacies inherent in the sharing economy. This exploration takes place within the extensive expanse of existing literature.

The study used the non-conventional method for data mining. An artificial intelligence (AI) tool called www.dimensions.ai was used to mine data between the year 2002 and 2021. After which the data was analysed, using Citespace software that assisted in building themes for answering the research questions.

The sharing economy has multifaceted implications for rural and urban destinations. For instance, the findings demonstrated that emotional solidarity fosters community bonds between tourists and residents, enhancing authenticity. While, management firms optimise short-term rentals, boosting revenue and occupancy rates despite capped at 20%. It further demonstrated that the sharing economy disrupts traditional accommodations, especially hotels, impacting rural and urban destinations differently based on location and regulatory flexibility. Technological advancements would shape the digital future, transforming the resource in sharing and connectivity in urban settings.

Management firms or agents significantly enhance property facilities, revenue and occupancy rates. Properties managed by professionals perform better in terms of revenue and occupancy; furthermore, traditional accommodations need innovative strategies to compete with sharing economy platforms. Policymakers must consider location-specific regulations to balance sharing economy impacts. Embracing technological advancements ensures urban destinations stay relevant and competitive.

Emotional solidarity fosters bonds between residents and tourists, contributing to a sense of community. Management firms contribute to local economies and stability. However, Airbnb's impact on traditional accommodations raises concerns about the effect on residents and communities.

The study incorporates classical sociology theory to understand emotional solidarity and extends the concept of moral economy to guide economic behaviour in the sharing economy. The analysis also underscores the influence of technological trends such as mobile technology, Internet of Things, AI and blockchain on sharing practices in reshaping existing theoretical frameworks in the sharing atmosphere. Furthermore, the co-creation of value theory highlights collaborative interactions between hosts and guests, shaping the sharing economy experience. Consumer segmentation and choice theories shed light on sharing economy dynamics. Institutional and location-based theories provide insights into regulatory and location-specific impacts.

This research contributes by comprehensively exploring the multifaceted implications of the sharing economy on a tourist destination. It delves into emotional solidarity, management firm roles and location-specific impacts, enriching the understanding of the sharing economy's effects. The application of co-creation of value theory and examination of platform technologies offer fresh perspectives on value creation and user engagement. The study's focus on practical dimensions guides stakeholders in optimising the benefits and addressing challenges posed by the sharing economy in urban contexts. The exploration of moral economy and its relevance to the sharing economy provides a novel perspective, while the examination of technological influences on sharing practices contributes to understanding the digital future of the sharing economy.

This study aims at modelling the enablers of dairy supply chain (DSC) in Indian context.

Interpretive structural modelling (ISM) approach has been used to model the enabler of dairy supply chain. The opinion has been taken from the industry experts and experienced academicians. Further, Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) used to classify the enablers based on driving and dependence power.

Findings show that stakeholder trust and top management support/leadership are the very crucial enablers in dairy supply chain; they are at a lower level of hierarchical structure and work as primary enablers to development of DSC. While customer satisfaction and financial performance are at top of the digraph, it shows these enablers are the outcome of a smooth supply chain. The MICMAC analysis suggests that the identified enablers are largely classified into dependent and independent enablers; there are no autonomous enablers in the dairy supply chain.

The study can aid businesses in the dairy processing industry in managing demand fluctuations, enhancing product quality, implementing effective information systems and adapting procedures, thereby enhancing supply chain performance.

There is very limited study on enablers of the dairy supply chain in general, while in the Indian context, there is no specific study on modelling the enablers of dairy supply chain.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

The concept of sustainable product design (SPD) is gaining significant attention in recent research. However, due to inherent uncertainties associated with new product development and incorporation of multiple qualitative and quantitative criteria; SPD is a complex and challenging task. The purpose of this paper is to introduce a novel approach by integrating quality function deployment (QFD), multi-criteria decision making (MCDM) technique and Six Sigma evaluation for facilitating SPD in the context of Industry 4.0.

The customer requirements are evaluated through the neutrosophic-decision-making trial and evaluation laboratory-analytic network process (DEMATEL-ANP)-based approach followed by utilizing QFD matrix to estimate the weights of the engineering characteristics (EC). The Six Sigma method is then employed to evaluate the alternatives’ design based on the ECs’ values.

The effectiveness of the suggested approach is illustrated through an example. The result indicates that utilization of the neutrosophic MCDM technique with integration of Six Sigma methodology provides a simple, effective and computationally inexpensive method for SPD.

The proposed approach is helpful in upstream evaluation of the product design with limited experimental/numerical data, maintaining a strong competitive position in the market and enhancing customer satisfaction.

This work provides a novel approach to objectively quantify performance of SPD under the paradigm of Industry 4.0 using the integration of QFD-based hybrid MCDM with Six Sigma method.

Hospital structures serve to protect and improve public health; however, they are recognized as a major source of environmental degradation. Thus, an effective performance evaluation framework is required to improve hospital sustainability. In this context, this study presents a holistic methodology that integrates the sustainability balanced scorecard (SBSC) with fuzzy Delphi method and fuzzy multi-criteria decision-making approaches for evaluating the sustainability performance of hospitals.

Initially, a comprehensive list of relevant sustainability evaluation criteria was considered based on six SBSC-based dimensions, in line with triple-bottom-line sustainability dimensions, and derived from the literature review and experts’ opinions. Then, the weights of perspectives and their respective criteria are computed and ranked utilizing the fuzzy analytic hierarchy process. Subsequently, the hospitals’ sustainable performance values are ranked based on these criteria using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution.

A numerical application was conducted in six public hospitals to exhibit the proposed model’s applicability. The results of this study revealed that “Patient satisfaction,” “Efficiency,” “Effectiveness,” “Access to care” and “Waste production,” respectively, are the five most important criteria of sustainable performance.

The new model will provide decision-makers with management tools that may help them identify the relevant factors for upgrading the level of sustainability in their hospitals and thus improve public health and community well-being.

This is the first study that proposes a new hybrid decision-making methodology for evaluating and comparing hospitals’ sustainability performance under a fuzzy environment.

To make more smart health-care system, the health-care data should be shared in the secure manner, and it improves health-care service quality. This paper aims to implement a modern decentralized blockchain, safe and easy-to-use health-care technology application in the cloud.

On observing the graph, the convergence analysis of proposed Levy Flight-integrated moth flame optimization method at 80th iteration was 4.59%, 2.80%, 3.316%, 8.92% and 2.55% higher than the traditional models MFO, artificial bee colony (ABC), particle swarm optimization (PSO), moth search algorithm (MSA) and glow worm swarm optimization (GWSO), respectively, for Hungarian data set. Particularly, in best case scenario, the adopted method attains low cost value (5.672671) when compared to all other traditional models such as MFO (5.727314), ABC (5.711577), PSO (5.706499), MSA (5.764517) and GWSO (5.723353).

The proposed method achieved effective performance in terms of key sensitivity, sanitization effectiveness, restoration effectiveness, etc.

This study aims to investigate the day-of-the-week (DoW) effect in globally listed private equity (LPE) markets using daily data covering the period 2004–2021.

To investigate the existence of the DoW effect in globally LPE markets, ordinary least squares regression, generalised autoregressive conditional heteroscedasticity (GARCH) regression and robust regressions are used. In addition, robustness audits are conducted by subdividing the sampling period into two sub-periods: pre-financial and post-financial crisis.

Limited statistically significant evidence is found for the DoW effect. By taking time-varying volatility into account, a statistically significant DoW effect can be observed, indicating that the DoW effect is driven by time-varying volatility. Economic significance is captured through visual inspection of average daily returns, which illustrate that Monday returns are lower than the other weekdays.

The results have important implications on whether to adopt a DoW strategy for investors in LPE. The findings show that higher returns on selected days of the week for certain indices are possible.

To the best of the author’s knowledge, this paper provides the first study to examine the DoW effect for globally LPE markets by using LPX indices and contributes valuable insights on this growing asset class.

The paper aims to examine the effect of justice perceptions of performance appraisal (JPPA) practices (i.e. distributive, procedural, informational and interpersonal justice) on organizational citizenship behavior (OCB) and affective commitment (AC) and the effect of AC on OCB. Further, it investigates the mediating role of AC in the relationship between JPPA practices and OCB. Moreover, this study examines the moderating effect of job level on the relationship between JPPA practices and OCB.

The data were collected using a self-reported structured questionnaire. A total of 650 questionnaires were distributed among the employees of 50 information technology (IT) companies in India, and 503 samples were obtained. The conceptual framework was tested using the partial least squares structural equation modeling (PLS-SEM) method, and the moderating effect was tested using process macro.

The findings of this study reveal that the JPPA practices positively affect OCB and AC and AC affects OCB. Further, AC partially mediates this relationship between JPPA practices and OCB. Furthermore, the direct effect of JPPA practices on OCB happens to be strengthened when the job level decreases, thus confirming the moderating role of job level.

The findings of this study augment the social exchange theory (SET) by suggesting that individuals perceiving justice or fairness in performance appraisal practices are likely to have a greater AC that ultimately engages employees in OCB.

This study will be helpful for human resource practitioners in IT companies who are responsible for the fairness of performance appraisal practices and expect their employees to be emotionally attached to the organization and engaged in OCB.

The study adds to the body of knowledge of how justice in performance appraisal practices links to OCB through AC and moderates by job level in an emerging economy in Asia.

Perishable inventory management for the grocery sector has become more challenging with extended omnichannel activities and emerging consumer expectations. This paper aims to identify and formalize key performance measures of omnichannel perishable inventory management (OCPI) and explore the influence of operational and market-related factors on these measures.

The inductive approach of this research synthesizes three performance measures (product waste, lost sales and freshness) and four influencing factors (channel effect, demand variability, product perishability and shelf life visibility) for OCPI, through industry investigation, expert interviews and a systematic literature review. Treating OCPI as a complex adaptive system and considering its transaction costs, this paper formalizes the OCPI performance measures and their influencing factors in two statements and four propositions, which are then tested through numerical analysis with simulation.

Product waste, lost sales and freshness are identified as distinctive OCPI performance measures, which are influenced by product perishability, shelf life visibility, demand variability and channel effects. The OCPI sensitivity to those influencing factors is diverse, whereas those factors are found to moderate each other's effects.

To manage perishables more effectively, with less waste and lost sales for the business and fresher products for the consumer, omnichannel firms need to consider store and online channel requirements and strive to reduce demand variability, extend product shelf life and facilitate item-level shelf life visibility. While flexible logistics capacity and dynamic pricing can mitigate demand variability, the product shelf life extension needs modifications in product design, production, or storage conditions. OCPI executives can also increase the product shelf life visibility through advanced stock monitoring/tracking technologies (e.g. smart tags or more comprehensive barcodes), particularly for the online channel which demands fresher products.

This paper provides a novel theoretical view on perishables in omnichannel systems. It specifies the OCPI performance, beyond typical inventory policies for cost minimization, while discussing its sensitivity to operations and market factors.