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The aim of this study was to analyze the effects of carbon reduction efforts and preservation efforts on system benefits in the cold chain industry of fresh products.

This study develops an optimal decision game model for the fresh products in the cold chain, incorporating the retailer's preservation effort and the supplier's carbon emission reduction effort. It quantifies the relationship between carbon emission reduction effort, preservation effort and system profit. The model considers parameters like carbon trading price, consumer low-carbon preference and consumer freshness preference, reflecting real-world conditions and market trends. Numerical simulations are conducted by varying these parameters to observe their impact on system profit.

Under the carbon cap-and-trade policy, the profit of the fresh cold chain system is higher than that of the fresh cold chain system without carbon constraints, and the profit of the supplier under decentralized decision-making is increased by nine times in the simulation results. The increase in carbon trading prices can effectively improve the freshness level of fresh products cold chain, carbon emission reduction level and system profit.

This study comprehensively considers the factors of freshness and carbon emission reduction, provides the optimal low-carbon production decision-making reference for the fresh food cold chain and promotes the sustainable development of the fresh food cold chain.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

Purpose: This study examines the effect of uncertainties on the hospitality industry from different perspectives across the globe. The hospitality industry faces several contemporary issues and challenges that have the potential to impact its growth and development. This study aims to analyse the current problems and uncertainties in the hospitality sector.

Need for the Study: The hospitality industry plays a significant role in the global economy with various services, including accommodation, food and beverage, events, and tourism. However, the sector faces several contemporary issues and challenges that have the potential to impact its growth and development. This study provides an overview of the most significant problems and challenges facing the hospitality industry today.

Methodology: A systematic literature review was conducted to identify and synthesise relevant studies on the effect of uncertainties issues on the hospitality industry. A systematic search of the Web of Science and Scopus databases was conducted to determine relevant studies published between 2010 and 2021. Studies were screened and selected based on pre-defined inclusion and exclusion criteria. A thematic analysis was performed to categorise the uncertainties and issues in the hospitality industry.

Findings: The study identified several uncertainties and issues facing the hospitality industry, including the pandemic uncertainties, financial crisis, whether positive and negative impacts, terrorism attacks on hotels and tourist places, uncertainties in government policies, situational risks like uncertainties, ambiguity, cultural differences, changes in tourist preferences and changing habits of the tourist.

This study aims to address three research questions pertaining to climate neutrality within the supply chain of metal and mining industry: (1) How can an organization implement practices related to climate neutrality in the supply chain? (2) How do members of the supply chain adopt different measures and essential processes to assist an organization in responding to climate change-related concerns? (3) How can the SAP-LAP framework assist in analyzing and proposing solutions to attain climate neutrality?

To address the proposed research questions concerning climate neutrality, this study employs a case study approach utilizing the SAP-LAP (situation, actor, process–learning, action, performance) framework. Within the SAP-LAP framework, adopting a natural resource-based perspective, the study thoroughly examines the intricacies and interactions among existing situations, pertinent actors and processes that impact climate initiatives within a metal and mining company.

The study's findings suggest that organizations can achieve the objective of climate neutrality by prioritizing resources and capabilities that lead to reduced GHG emissions, lower energy consumption and optimal resource utilization. The study further proposes key elements that significantly influence the pursuit of climate neutrality within enterprises.

This study is one of the earliest contributions to the development of a holistic understanding of climate neutrality in the supply chain of the metal and mining industry.

The study will assist practitioners and policymakers in comprehending the present circumstances, actors and processes involved in enterprises' supply networks in order to attain climate neutrality in supply chains, as well as in taking the right steps to enhance performance.

This study presents a climate neutrality model and provides valuable insights into emission management, contributing to the achievement of the climate neutrality objective.

This paper aims to identify how a public client’s use of control systems (process, output and social control) affect innovation possibilities in construction projects.

Semi-structured interviews about six infrastructure projects were conducted to identify respondents’ views on innovation possibilities. These possibilities were then analyzed from an organizational control perspective within principal–agent relationships between the Swedish Transport Administration (STA) and their contractors.

How the client uses control systems affects innovation possibilities. Relying on process control could negatively affect innovation opportunities, whereas output control could have a positive influence. In addition, social control seems to have a weak effect, as the STA appears not to use social control to facilitate joint innovation. Public clients must comply with the Public Procurement Act and, therefore, retain the requirements specified in the tendering documents. Much of the steering of the execution is connected to the ex ante phase (before signing the contract), which affects innovation possibilities in the design and execution phases for the contractor.

This study was conducted with only one client, thus limiting its generalizability. However, the findings provide an important stepping stone to further investigation into balancing control systems and creating innovation possibilities in a principal–agent relationship.

Although public procurement has increasingly been emphasized as a major potential source of innovation, studying how a public client’s use of organizational control systems affects innovation possibilities in the construction sector has received scant attention.

This study aims to examine the interaction effects of chatbots’ language style and customers’ decision-making journey stage on customer’s service encounter satisfaction and the mediating role of customer perception of emotional support and informational support using the construal level theory and social support theory as conceptual frameworks.

This study used a scenario-based experiment with a 2 (chatbot’s language style: abstract language vs concrete language) × 2 (decision-making journey stage: informational stage vs transactional stage) between-subjects design.

Findings show that during the informational stage, chatbots that use abstract language style exert a strong influence on service encounter satisfaction through emotional support. During the transactional stage, chatbots that use concrete language style exert a strong impact on service encounter satisfaction through informational support.

Findings provide some suggestions for improving customer–chatbot interaction quality during online service encounters.

This study offers a novel perspective on customer interaction experience with chatbots by investigating the chatbot’s language styles at different decision-making journey stages.

This study aims to determine the contributing factors to technical knowledge valuation at the related quadruple levels of commercialisation, including the idea, benchtop technical knowledge, prototype technical knowledge and commercialised technical knowledge, and then classify the factors by the valuation objectives.

The study method is descriptive-causal, and documentation tools of published scientific research articles in authentic local and international journals were used to extract the contributing factors to technical knowledge valuation. Moreover, the Likert spectrum-based questionnaire is used to determine the weight of each determined component. On the other hand, hierarchical analysis is used based on the extracted results from the distributed classification questionnaire among scholars to determine the allocable weight of each component.

The results indicate that at the idea step, the highest ranks among the contributing factors to technical knowledge valuation are for the indicators of innovation rate enhancement, novelty, creation of new products, profitability growth and dependence decline. In the benchtop technical knowledge step, the indicators of profitability growth, product quality enhancement, novelty, production risk drop, innovation rate enhancement, production costs drop, product price competitiveness and independence from rare machinery have the highest impact coefficients on valuation. Moreover, the prioritisation of factors in prototype technical knowledge shows that the indicators of productive risk decline, infrastructure, decrease in product delivery time, productivity growth and profitability growth are the most critical factors in technical knowledge valuation. Finally, profitability growth factors, production cost drop, productive risk drop, creating a new product, product price competitiveness and dependence decline determine the most valuable technical knowledge in the commercialisation phase.

The most salient innovation of the study involves the development levels of technical knowledge in the commercialisation cycle for determining the contributing factors to technical knowledge valuation and using multivariate decision-making methods to classify the so-called factors. The major limitation can be the context of the study because the paper was carried out by Iranian assessors and specialists using the experiences, opinions and approaches of opinion leaders based on the dominant social, cultural and accounting background of a developing country, not a developed one.

This paper is applicable because it elucidates the technical knowledge valuation factors for managers and owners of technological and knowledge-based companies to facilitate value determination and register the technical knowledge of innovative products in financial statements for the logical presentation of available intangible assets in the economic unit. Besides, in the high-tech area, collecting information from the contributing factors to technical knowledge valuation provides an opportunity to support intellectual property rights and facilitate transaction processes. Finally, in legal areas, in cases of breaching intellectual property rights relative to technical knowledge, the determination of technical knowledge value provides a solid basis for estimating the damage rate.

This paper presents a comprehensive decision-making framework designed for family-owned hotels, specifically focusing on evaluating and selecting suppliers and strategic partners, with a particular emphasis on Iranian holding companies and five-star hotels.

The research emphasizes the unique position of family-owned hotels as not only commercial enterprises but also embodiments of tradition, personal touch and community engagement, which sets them apart in a competitive market. Through a detailed literature review, methodology and analysis, including fuzzy analysis and the TOPSIS method, the study systematically evaluates various criteria crucial for selecting suppliers and strategic partners.

The framework evaluates criteria such as price competitiveness, quality of products/services, reliability and timeliness, flexibility and scalability, communication and responsiveness, after-sales service and support, ethical and sustainable practices, technology and innovation, and compatibility with business culture. By integrating these parameters, the framework addresses both operational needs and strategic objectives, ensuring chosen suppliers and partners align with the hotels' core values and operational requirements.

The study offers valuable insights for family-owned hospitality businesses to navigate supplier and strategic partner selection, and opens avenues for future research, particularly in adapting to technological advancements, sustainability practices and the evolving dynamics of the hospitality industry.

The research underscores the significance of family-owned hotels in fostering tradition, personal connection and community engagement, contributing to the social fabric of the hospitality industry.

This paper provides a unique perspective on supplier and strategic partner selection, tailored for family-owned hotels and offers a comprehensive framework that integrates both operational needs and strategic objectives, ensuring alignment with core values and requirements.

This research aims to determine the ideal fare for various aircraft itineraries by modeling prices using a neural network method. Dynamic pricing has been studied from the airline’s point of view, with a focus on demand forecasting and price differentiation. Early demand forecasting on a specific route can assist an airline in strategically planning flights and determining optimal pricing strategies.

A feedforward neural network was employed in the current study. Two hidden layers, consisting of 18 and 12 neurons, were incorporated to enhance the network’s capabilities. The activation function employed for these layers was tanh. Additionally, it was considered that the output layer’s functions were linear. The neural network inputs considered in this study were flight path, month of flight, flight date (week/day), flight time, aircraft type (Boeing, Airbus, other), and flight class (economy, business). The neural network output, on the other hand, was the ticket price. The dataset comprises 16,585 records, specifically flight data for Iranian airlines for 2022.

The findings indicate that the model achieved a high level of accuracy in approximating the actual data. Additionally, it demonstrated the ability to predict the optimal ticket price for various flight routes with minimal error.

Based on the significant alignment observed between the actual data and the tested data utilizing the algorithmic model, airlines can proactively anticipate ticket prices across all routes, optimizing the revenue generated by each flight. The neural network algorithm utilized in this study offers a valuable opportunity for companies to enhance their decision-making processes. By leveraging the algorithm’s features, companies can analyze past data effectively and predict future prices. This enables them to make informed and timely decisions based on reliable information.

The present study represents a pioneering research endeavor that investigates using a neural network algorithm to predict the most suitable pricing for various flight routes. This study aims to provide valuable insights into dynamic pricing for marketing researchers and practitioners.

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.