Search results

Since conducting agile strategies provides sustainable passenger satisfaction and revenue by replacing applied policies with more profitable ones rapidly, the focus of this study is to evaluate agile attributes for managing low-cost carriers (LCCs) operations by means of resources and competences based on dynamic capabilities built on resource-based view (RBV) theory and to achieve sustainable competitive advantage in a volatile and dynamic air transport environment. LCCs in Turkey are also evaluated in this study since the competition among LCCs is high to gain market share and they can adapt quickly to all kinds of circumstances.

Two well-known Multi-Criteria Decision-Making Methods (MCDM) named as the Stepwise Weight Assessment Ratio Analysis (SWARA) and multi-attributive border approximation area comparison (MABAC) methods by employing Picture fuzzy sets (PiFS) are employed to determine weight of agile attributes and superiority of LCCs based on agile attributes in the market, respectively. To check the consistency and robustness of the results for the proposed approach, comparative and sensitivity analysis are performed at the end of the study.

While the ranking orders of agile attributes are Strategic Responsiveness (AG1), Financial Management (AG4), Quality (AG2), Digital integration (AG3) and Reliability (AG5), respectively, LCC2 is selected as the best agile airline company in Turkey with respect to agile attributes. SWARA and MABAC method based on PiFS is appropriate and effective method to evaluate agile attributes that has important reference value for the airline companies in aviation industry.

The findings of this study will support managers in the airline industry to conduct airline operations more flexibly and effectively to take sustainable competitive advantage in unexpected and dynamic environment.

To the author' best knowledge, this study is the first developed to identify the attributes necessary to increase agility in LCCs. Thus, as a systematic tool, a framework is developed for the implementation of agile attributes to achieve sustainable competitive advantage in the airline industry and presented a roadmap for airline managers to deal with crises and challenging situations by satisfying customer and increasing competitiveness.

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.

The study aims to facilitate the medical tourists visiting emerging countries for various kinds of ailments by ranking the possible destinations to avail medical treatments.

A Fuzzy Analytical Hierarchical Process (FAHP) with a mixed-method approach is applied to analyze data collected from patients and substantiate it with medical tour operators in India to gain managerial insights on the choice-making patterns of the patients.

India is a preferred emerging market location due to the low cost and high medical staff quality. India offers value for money, whereas Singapore and Thailand are preferred destinations for quality and technology.

The study will facilitate the emerging markets' governments, hospitals and medical tourists to understand the importance of various determinants responsible for availing medical treatment outside their country.

The study recommends that cost and quality care are the patients' prime focus; government policies must provide clear guidelines on what the hospitals and country environment can offer and accordingly align the marketing strategies.

This study is the first attempt to rank various factors affecting medical tourism using the FAHP approach.

Because of the importance of the wheat industry in the economy, a real-featured performance measurement approach is essential for the wheat production process. The purpose of this paper is to develop a data envelopment analysis (DEA) model that is fully compatible with the wheat production process so that managers and farmers can use it to evaluate the efficiency of wheat farms for strategic decisions.

A dynamic multi-stage network DEA model is developed to evaluate the efficiency of wheat production farms in short-term (two-year) and long-term (eight-year) periods.

The results of this study show that because of the lack of long-term planning and excessive reliance on rain, most of the investigated regions have no stability in efficiency, and the efficiency of the regions changes in a zigzag manner over time. Among studied regions, only the Hashtrood region has high and stable efficiency, and other regions can follow the example of this region's cultivation method.

To the best of the authors’ knowledge, this study is the first one that uses the dynamic multi-stage network DEA considering every other year cultivation method and direct–indirect inputs in the agricultural section.

This chapter analyzes the efficiency levels of a circular economy (CE) with an emphasis on transaction costs. It examines the governance aspect of CE activities in comparison to the predominant linear value creation. Extant CE research in business studies tends to be descriptive and lacks a theoretical foundation, particularly in understanding CE management. Transaction cost theory explains efficiency in economic organizing, lending itself to the study of arrangements that maximize resource efficiency at continued economic virtue. The conceptualization proposes that CE transaction costs are greater than those within the linear economy (LE), primarily due to the uncertainties about reciprocal dependencies, looping material complexities, exchanging novel information, and increased contracting efforts. Geographically bounded and institutionally homogeneous CE initiatives may curb these rising costs. By bringing efficiency concerns into CE analysis, the chapter demonstrates the applicability of transaction cost theory and highlights CE relevance to international business by pointing out spatial choice implications.

The present paper concerns the domestic and international accreditations of business schools to find the commonalities in various accreditation standards. The study also addresses cost involvement and comparisons between various accreditations.

The research examined the requirements, methods and preparations for many national and international accrediting authorities. The accreditation criteria, history and guidelines were collected from secondary data sources. The content analysis was used to draw conclusions about the similarities and differences between the data sources and identify any differences and similarities between various accreditations.

The study found commonalities across accreditation standards and cost analyses, helping institutions achieve suitable accreditations. To unify the administrative procedure at the institutional level and reduce duplicates for schools seeking multiple accreditations, similarities in all accreditation requirements are analysed. The comparison helps organisations determine which accreditation standard best suits their needs and goals. Schools with limited budgets might compare accreditation costs to determine which are worth pursuing.

This study is limited to comparing a few accreditation bodies related to management education. In future research, the study may be extended to other areas. The use of the approach developed in this study for evaluating accreditation agencies of different streams, such as engineering, science and medicine, will be the study's future implications.

The research indicates that schools can streamline accreditation by identifying commonalities, aligning procedures, comparing standards and conducting cost assessments. These insights aid efficient accreditation and inform accrediting bodies' framework enhancements. Standards facilitate global performance comparisons.

This is likely the first study to compare Indian and international accreditations using the accreditation frameworks and cost analyses using comparative analysis. The study recommends strategies for achieving academic benchmarks through continuous improvement activities and success in international competition.

Interest rate derivatives (IRDs) are the essential components of financial risk management and are used across various industry sectors. The objective is to analyze the differences in approach to managing interest rate risks between the Indian corporates that execute IRDs and the ones that do not.

Interest rate fluctuations require Indian corporates to hedge their exposures in foreign currency interest rates. This is all the more true for mid-sized corporates because of their balance sheet exposures. Additionally, they may not have the resources to formulate risk management policies. This paper analyzes data collected from financial statements of a diverse set of companies that use IRD and helps in formulating such a strategy.

The results indicate significant differences for some of the parameters like information asymmetry and agency cost between users and non-users of IRDs. The study further suggests causality between users of derivatives and parameters like size, growth and debt.

The study compares users and non-users of IRDs, thereby identifying factors unique to users of IRDs. It also studies causality relations which explain the motivation to do IRDs. Thus, it enables risk managers to use this as a reference point to decide on their strategies.

While there are multiple studies across geographies and sectors including commercial banks in India on the usage of interest rate swaps, this study focuses on Indian mid-tier corporates. Furthermore, the study distinguishes between users and non-users based on financial parameters, which in turn would provide a framework for decision-hedging strategies.

This research explores mitigating carbon emissions and integrating sustainability in e-commerce logistics by optimizing the multi-depot pollution routing problem with time windows (MDPRPTW). A proposed model contrasts non-collaborative and collaborative decision-making for order assignment among logistics service providers (LSPs), incorporating low-carbon considerations.

The model is substantiated using improved adaptive large neighborhood search (IALNS), tabu search (TS) and oriented ant colony algorithm (OACA) within the context of e-commerce logistics. For model validation, a normal distribution is employed to generate random demand and inputs, derived from the location and requirements files of LSPs.

This research validates the efficacy of e-commerce logistics optimization and IALNS, TS and OACA algorithms, especially when demand follows a normal distribution. It establishes that cooperation among LSPs can substantially reduce carbon emissions and costs, emphasizing the importance of integrating sustainability in e-commerce logistics optimization.

This paper proposes a meta-heuristic algorithm to solve the NP-hard problem. Methodologies such as reinforcement learning can be investigated in future work.

This research can help logistics managers understand the status of sustainable and cost-effective logistics operations and provide a basis for optimal decision-making.

This paper describes the complexity of the MDPRPTW model, which addresses both carbon emissions and cost reduction. Detailed information about the algorithm, methodology and computational studies is investigated. The research problem encompasses various practical aspects related to routing optimization in e-commerce logistics, aiming for sustainable development.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.