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Relative performance evaluation (RPE) is a widely practiced employee appraisal process in the services industry. In a global delivery model, teams are spread across different geographical locations. The team members work on various tasks under the guidance of different managers and at times under more than one manager for performing the same task. Such complexities make RPE of the team members quite challenging. The paper proposes a methodical step-by-step approach to simplify the evaluation process without compromising on the rigour.

RPE has followed three different approaches. First is the traditional way, wherein evaluators had a common meeting to discuss and arrive at relative evaluation and ranking of members of the peer group employees. In the second, the number of evaluators and employees in a peer group were split in to 2 subgroups. The evaluators provided independent ratings and rankings. Simple mathematical tool then derived the combined ranking. In the third approach, each evaluator evaluated each employee in the peer group and provided the relative ranking for each employee. Again, mathematical tools provided the final ranking considering inputs from all evaluators. All the three evaluation approaches were analysed through an inter-rater agreement method.

All the three approaches for evaluation provided similar results giving confidence that less time-consuming methods could be adopted by evaluators without compromising on the rigour of the evaluation. The outcome of the exercise proved effective as the complaints reaching the ombudsmen reduced as compared to the earlier years. Considerable evaluation time was also saved. The study described in this paper is carried out in a non-unionized, Indian private sector services firm. Its effectiveness in other set ups is yet to be tested.

The research is carried out in the Indian Engineering services firm operating in the Knowledge based sector. Though study results are encouraging, the adaptability of methodology across different sectors and geographies is yet to be tested. More broad based studies are needed to evaluate suitability across firms and regions.

Relative evaluation exercise is challenging for evaluators. Although openness in evaluation is desired, it also makes evaluators uncomfortable in appearing to be taking sides or being opposing a candidate's ranking. The proposed approach brings in anonymity to each evaluator without scarifying individual evaluation.

The proposed methodology can be deployed across different services industries as the proposed methodology is business domain agnostic. It can be easily ported and tailored to align with an individual organization's evaluation philosophy. The suitability and effectiveness of the method can be studied under various types of firms like manufacturing, private, public, NGO, labour oriented, etc. As the proposed method reduces efforts, the stake holders can focus on understanding the relation between employee performance measurement, employee engagement, and long-term outcomes related to employee performance evaluation.

The proposed employee evaluation method leverages inter-rater reliability and agreement tool as a consensus approach to the relative performance ranking exercise. Such an approach to relative performance ranking is original as no prior studies with such an approach are found in the existing Literature.

Due to the current pandemic, the importance of logistics functions and decisions is well understood both at the level of companies and users. Logistics systems and related decisions are of vital importance in making supply chains effective, efficient and without disruption. Logistic pressure factors may emerge at different points along the logistics process, and given the role of logistics decisions as one of the important indicators of competitiveness, the determination of the logistics pressures that are likely to increase the costs of business, and their causative factors are a vital aspect of the logistics decision-making process. The study aims to provide assistance in the selection of the most ideal logistics decision by ranking the pressure factors affecting the logistics system, especially during the pandemic period for logistics enterprises operating in Ordu and Giresun provinces and which have a corporate identity.

In this study, it is aimed to make the most ideal logistics decision selection by ranking the pressure factors affecting the logistics system, especially during the pandemic period for the logistics enterprises operating in Ordu and Giresun provinces and having a corporate identity. For that purpose interval-valued Pythagorean fuzzy (IVPF)–analytic hierarchy process (AHP) based combinative distance-based assessment (CODAS) methodology was used. Additionally sensitivity and comparison analysis were discussed.

Competitive pressure was found as the most important pressure factor affecting the logistics system during the pandemic period. Change in regulatory rules was the pressure factor found to have the least effect on the logistics system. Using the weights of logistics pressure factors, “Operational Decisions” was found to be the most ideal logistics decision selection.

The findings provide support for the evaluation of logistical pressures and decision options by presenting a decision model capable of processing ambiguous information. During a pandemic or similar period, the study assists decision makers in determining a new route. The findings will also call business managers' attention to logistical pressure factors and lead them toward more realistic and feasible practices in the logistics decision-making process.

This study provided an effective and applicable solution to a decision-making problem in the logistics sector including logistics pressure factors and the selection of logistics decisions. In this context, a methodology was presented that will allow businesses to self-evaluate their own logistics pressure factors and the selection of optimal solutions.

This study sought to understand which food allergen labeling systems (non-directive, semi-directive, and directive) were attended to and preferred by 34 participants with food hypersensitivity and their perceived corporate social responsibility (CSR) and behavioral intention towards a restaurant that identifies food allergens on menus.

This study used an online survey with open-ended and ranking questions, combined with eye-tracking technology, to explore participants' visual attention and design preferences regarding four menus. This study utilized one-way repeated measures analysis of variance (RM-ANOVA) and heat maps to analyze participants' menu-reading behaviors. A content analysis of survey responses and a ranking analysis of menus were conducted to understand the reasons behind consumers' preferred menu designs.

The advisory statement was not much attended to. Participants identified food allergen information significantly quicker with the directive labeling system (icons) than the other two systems, implying they were eye-catching. Semi-directive labeling system (red text) has lower visit count and was more preferred than two other systems; each labeling system has its strengths and limitations. Participants viewed restaurants that disclosed food allergen information on menus as socially responsible, and they would revisit those restaurants in the future.

This study was one of the first to explore, through use of eye-tracking technology, which food allergen labeling systems were attended to by consumers with food hypersensitivity. The use of triangulation methods strengthened the credibility of the results. The study provided empirical data to restauranteurs in the US on the values of food allergen identification on restaurant menus, although it is voluntary.

Growing concern over sustainability adoption has presented an array of challenges to businesses. While vital to an economy's success, banking is not immune to societal, environmental and economic consequences of business practices. The study has examined the sustainable performance of banking institutions on the suggested multidimensional framework comprising economic, environmental, social, governance and financial dimensions and 52 sustainability indicators. The study benchmarks the significant performance indicators of leading banks indispensable to sustainable banking performance. The findings attempt to address research questions concerning the extent of sustainable banking performance, ranking the sustainability dimensions and indicators and standardizing sustainability adoption metrics.

To determine the responsiveness of the banking industry to sustainability dimensions, content analysis was conducted using NVivo software for the year 2021–2022. Furthermore, a hybrid multicriteria decision-making (MCDM) approach is used by integrating entropy, the technique for order preference by similarity to ideal solution (TOPSIS) and VlseKriterijumska Optimizacija KOmpromisno Resenje (VIKOR) to provide relative weights to performance indicators and prioritize banks based on their sustainable performance. Sensitivity analysis is used to ensure the robustness of results.

In the context of the Indian banking industry, the pattern of sustainability reporting is inconsistent and concentrated on addressing environmental and social concerns. The results of the entropy methodology prioritized “Environmental” sustainability over other selected dimensions while “Financial” dimension has been assigned the least priority in the ranking order. The significant sustainable performance indicators delineated in this study should be used as standards to ensure the accountability and credibility of the sustainable banking industry. Additionally, the research findings will provide valuable inputs to policymakers and regulators to assure better contribution of the banking sector in meeting sustainability goals.

Considering the paucity of studies on sustainable banking performance, this study makes two significant contributions to the literature. First, the suggested multidimensional disclosure model integrating financial and nonfinancial indicators would facilitate banking institutions in addressing the five aspects of sustainability. As one of the first studies in the context of the Indian banking industry, the findings would pave the way for better diffusion of sustainability practices. Second, the inclusion of MCDM techniques prioritizes the significance of sustainability indicators and benchmarks the performance of leading banks to achieve better profits and more substantial growth.

Rice is an important grain in Indian scenarios, and the purpose of the research work is to identify the attributes which can be the possible barriers in the traditional rice supply chain network.

A multilevel conceptual model is developed based on the literature review, and a field study is conducted by administering a questionnaire from the experts. Fuzzy logic methodology and a ranking score method is applied to identify the rice supply chain performance and the barriers of the traditional rice supply chain network.

The rice supply chain performance index for the traditional rice supply chain network is obtained, and the performance of the existing rice supply chain is found to be “fair”. The “information flow” is the attribute that can be a critical weak attribute in the traditional rice supply chain network. A proposed model of the blockchain technology-enabled rice supply chain network is developed as a solution for the “information flow” barrier.

The present research work is focussed on the generalized rice supply chain model of the Indian scenario, and more detailed studies can be carried out based on the regional issues.

The rice supply chain plays an important role in Indian economic development, and hence the current research paper focusses on identifying the barriers and the performance of the existing rice supply chain network.

The objectives of this paper are twofold. First, it aims to investigate the impact of the COVID-19 pandemic on the activities and performance of small and medium-sized enterprises (SMEs) government suppliers involved in government green procurement (GGP). Second, it examines the differences in the impact of COVID-19 between small and medium-sized groups.

The study used a questionnaire survey that was distributed to SMEs listed in the MyHIJAU directory that supply green goods and services to the government. Of the total 394 sample respondents, 126 usable questionnaires were received, representing a usable response rate of 31.98%. Descriptive analysis of mean score, standard deviation and mean score ranking was used to analyse the overall results. A t-test analysis was carried out to examine the differences between the small and medium-sized groups of companies.

The study discovers that the SME government suppliers involved in GGP were impacted by the COVID-19 pandemic. The top ranked impacts are that “the COVID-19 pandemic has heightened health and safety practices among the employees”, “the COVID-19 pandemic has reduced company’s turnover”, “the COVID-19 pandemic has forced the company to implement a cost reduction strategy”, “the COVID-19 pandemic has had a negative impact on the company’s ability to deliver work, supplies or services to the government” and “the COVID-19 pandemic has forced the company to incur higher production costs for green products or services provided”. However, there is no significant difference between the impact of the COVID-19 pandemic on the small and medium-sized group of enterprises.

The present study is among the fewer studies on the impact of the COVID-19 pandemic, with particular focus on SME government suppliers involved in GGP.

The digital direct-to-consumer (D2C) approach has seen widespread adoption across many industries, but its application to regional food products has been slower. This study aims to identify and rank the most significant challenges to the widespread use of digital D2C for regional food products.

A multi-criteria decision-making method called a fuzzy analytic hierarchy process (FAHP) was used to determine the order in which these obstacles were evaluated. Thirty-five barriers were identified and categorized into six categories named technological, operational, sociocultural, financial, market and institutional and regulatory barriers.

Market barriers ranked as the top barrier, and the technological barrier ranked the least significant amongst the main barrier categories for the adoption of digital D2C model for the regional food products. Lack of consumer awareness ranked number one globally, and lack of government subsidies ranked the least amongst the thirty-five identified barriers. Operational barriers came out to be second most significant barrier followed by institutional and regulatory barrier, sociocultural barrier, financial barrier and technological barrier.

The findings of this research were derived through a numerical examination of data gathered from the Indian setting. It follows that the technological, sociocultural, financial, market, operational and institutional constraints, among others, outlined here are all unique to India. Because of the unique nature of the Indian setting, the results of this study can only be used there and not elsewhere. It is possible that future research will broaden the aims of this one and refine its methodology. Digital D2C adoption for regional food products may be prioritized and ranked using quantitative and qualitative data sources like ANP and TOPSIS. It is possible that similar studies may be conducted in nations which have a different set of operational, technological, sociocultural market and financial and regulatory barriers. Conceptual framework can be formed by integrating TAM and TPB to understand the buying behavior of regional food products via digital D2C.

This research is the first to identify challenges to the widespread use of the digital D2C model for regional food products. Policymakers and other interested parties can use this information to better understand the difficulties of expanding the distribution of regional food products beyond their immediate regions.

This exploratory study aims to examine how wine stakeholders' are adopting innovative advancements in smart industry 4.0 application technology (SI4.0AT) coupled with electronic word-of-mouth (e-WOM) techniques to increase winery visitation and wine sales, prior to and during a global pandemic in the Yarra Valley in Victoria, Australia.

A qualitative interpretivist geographical bounded case study approach was used to collect data from fifteen Victorian wine stakeholder's prior to and during the global pandemic COVID-19. A thematic analysis was applied to interpret participant responses and how they viewed, reacted to and harnessed S14.0AT and e-WOM to continue and increase wine sales.

The findings suggest few wine stakeholders' were actively implementing SI4.0AT prior to the global pandemic, COVID -19. With the forced closure of wineries to visitors across Australia, most small to medium-sized wineries immediately changed their business models to concentrate on domestic e-commerce wine sales and home delivery. To support these new business models, e-WOM techniques were quickly adopted or increased whereas other S14.0AT technologies were not, due to financial restraints.

The number of participants used in this study is a limitation, however, this can be overcome by replicating this study in other wine regions. This research focused on the wine stakeholders' viewpoint only and may not be easily generalised. Future studies may examine the “what” aspect of SI4.0AT is being used and e-WOM content such as investigating what consumers are saying about these wineries, the method of communication and the motivations for consumers to engage with a winery.

The implications for the wine industry and overall results offer insight into the wine stakeholders' perceptions of SI4.0AT and e-WOM and consequent marketing strategy of wineries in the Yarra Valley, Victoria, both pre- and during forced face-to-face winery closures due to a global pandemic. Wineries need to continue to harness and leverage the benefits of e-WOM wine marketing in their marketing strategies and continue to explore the affordability of 4.0 app technology and Tourism 4.0 options.

This study is the first attempt at investigating how wine stakeholders' view; react to and harness SI4.0AT and e-WOM through formalised online marketing strategies which should continue to be investigated in the future.

This paper aims to propose a bed and breakfast (B&B) recommendation method that takes into account review timeliness and user preferences to help consumers choose the most satisfactory B&B.

This paper proposes a B&B ranking method based on improved intuitionistic fuzzy sets. First, text mining and cluster analysis are combined to identify the concerns of consumers and construct an attribute set. Second, an attribute-level-based text sentiment analysis is established. The authors propose an improved intuitionistic fuzzy set, which is more in line with the actual situation of sentiment analysis of online reviews. Next, subjective-objective combinatorial assignments are applied, considering the consumers’ preferences. Finally, the vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) algorithm, based on the improved score function, is advised to evaluate B&Bs.

A case study is presented to illustrate the use of the proposed method. Comparative analysis with other multi-attribute decision-making (MADM) methods proves the effectiveness and superiority of the VIKOR algorithm based on the improved intuitionistic fuzzy sets proposed in this paper.

Proposing a B&B recommendation method that takes into account review timeliness and user customization is the innovation of this paper. In this approach, the authors propose improved intuitionistic fuzzy sets. Compared with the traditional intuitionistic fuzzy set, the improved intuitionistic fuzzy set increases the abstention membership, which is more in line with the actual situation of attribute-level sentiment analysis of online reviews.

Subway systems are highly susceptible to external disturbances from emergencies, triggering a series of consequences such as the paralysis of the internal network transportation functions, causing significant economic and safety losses to cities. Therefore, it is necessary to analyze the factors affecting the resilience of the subway system to reduce the impact of disaster incidents.

Using the interval type-2 fuzzy linguistic term set and the K-medoids clustering algorithm, this paper improves the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to construct a subway resilience factor analysis model for emergencies. Through comparative analysis, this study confirms the superior performance of the proposed approach in enhancing the precision of the DEMATEL method.

The results indicate that the operation and management level of emergency command organizations is the key resilience factors of subway operations in China. Furthermore, based on real case analyses, the corresponding suggestions and measures are put forward to improve the overall operation resilience level of the subway.

This paper identifies four emergency scenarios and 15 resilience factors affecting subway operations through literature review and expert consultation. The improved fuzzy DEMATEL method is applied to explore the levels of influence and causal mechanisms among the resilience factors of the subway system under the four emergency scenarios.