Search results

Channel coordination has become an essential part of researching hotel supply chain management practices. This paper develops an improved channel coordination approach to coordinate the profit distribution between hotels and online travel agencies (OTAs) achieved through an introduction of advertising fees. This direction further improves the decentralization of cooperation and achieves Pareto improvement to achieve mutual profitability.

The methodology used in this study involves Stackelberg game theory employed for the decision-making and analysis of both the hotel and OTA. The OTA, acting as the leader, offers a hotel a contract specifying the commission rate that the hotel will pay to the respective OTA. The hotel, acting as a follower, sets a self-interested room rate as a given response. A deterministic, price-sensitive linear demand function is utilized to derive possible analytical solutions once centralized, noncooperative decentralization and cooperative decentralized channel occurs.

Results show that a new channel coordination approach is possible, namely via advertising fees. Prior to channel coordination, the OTA tends to set a higher commission rate, and the hotel sets a higher room rate in response under noncooperative decentralization. As such, this results in a lower channel-wide profit for all. One way to reduce channel-wide profit loss is to use a method of cooperative decentralization, which can, and will result in optimal profit as centralization takes place. However, the lack of incentives makes cooperative decentralization unfeasible. Further improvement is possible by using advertising fees based on a cooperative decentralization agreement, which can reach Pareto improvement.

This paper helps the OTA industry and hotel owners cooperate by way of smoother coordination. This study provides practitioners with two important practical implications. The first one is that the coordination between the hotel industry and OTA through cooperative decentralization allows for the achievement of higher profitability than that of noncooperative decentralization. The second one is that this paper solves the outstanding problem of insufficient incentives characteristic of cooperative decentralization by means of an advertising fee as a new supply chain coordination approach.

This paper offers both the problem and solution regarding the lack of incentives that hamper cooperative decentralization without the use of advertising fees. This paper is unique in that it derives analytical solutions regarding commissions levied in a typical hotel supply chain under noncooperative decentralization.

The purpose of this study is developing a new buy-back coordination contract in the symbiotic supply chain. In this new contract, the goal of the supply chain members (profit maximization) is realized.

This paper encourages the manufacturer to order products optimally by presenting a new buy-back coordination contract, and in return, the supplier undertakes to buy the unsold products from the manufacturer at the buy-back price. By using data-driven decision-making and multiobjective decision-making and considering the existing conditions in the symbiosis industry, a contract has been presented that guarantees the profits of supply chain members.

In this paper, it was found out how the authors can determine the order quantity, buy-back price and wholesale price in a symbiotic supply chain in such a way that it makes a profit for both the supplier and the manufacturer. In other words, how to determine these variables to encourage the manufacturer to order more quantity to the supplier so that both will benefit.

To the best of the authors’ knowledge, this is the first paper that defines a new buy-back coordination contract in the symbiotic supply chain by considering uncertain demand and a multiobjective model. Due to the importance of environmental issues, the sharing of resources by companies and organizations with each other, and the necessity of their cooperation, industries are moving toward a symbiosis industry.

Food quality and safety issues have always been imperative topics discussed by people. The anti-tampering of blockchain technology and the transparency of information make it possible to improve food traceability and safety quality. How to effectively apply blockchain traceability technology to food safety has great research significance for improving food safety and consumer quality trust.

The paper aims to analyze the differences in product quality levels and market participants’ profits before and after the use of blockchain-driven traceability technology in the food agricultural product supply chain (SC) in the dynamic game frameworks of supplier-led and retailer-led modes, respectively, and explores the willingness, social welfare and consumer surplus of each member of the agricultural product SC to participate in the blockchain. Besides, We investigate the SC performance improvement with the mechanism of central centralized decision-making and revenue-sharing contract, compared to the SC performance in dynamic games.

The results are obtained as follow: The adoption of blockchain traceability technology can help improve the quality of food agricultural products, consumer surplus and social welfare, but the application and popularization of technology is hindered by traceability technology installment costs. Compared with the supplier leadership model, retailer-led food quality level, customer surplus and social welfare are higher.

How to effectively apply blockchain traceability technology to food safety has great research significance for improving food safety and consumer quality trust.

Food quality and safety issues have always been hot topics discussed by people. The anti-tampering of blockchain technology and the transparency of information make it possible to improve food traceability and safety quality.

The research results enrich the theories related to food safety and quality, and provide a valuable reference for food enterprises involved in the decision-making exploration of blockchain technology.

Based on the characteristics of blockchain technology, the demand function is adjusted and the product loss risk of channel members is transferred through a Stackelberg game SC composed of agricultural products suppliers and retailers.

• •

  We introduce two features of blockchain: quality trust and product information tracking.

• •

  The willingness of each member of the supply chain to use blockchain for product traceability was explored.

• •

  The overall traceability effect of the retailer-led blockchain is better than that of the manufacturer-led blockchain.

• •

  The cost of blockchain technology is a barrier to its adoption.

• •

  Blockchain brings higher consumer surplus and social welfare.

We introduce two features of blockchain: quality trust and product information tracking.

The willingness of each member of the supply chain to use blockchain for product traceability was explored.

The overall traceability effect of the retailer-led blockchain is better than that of the manufacturer-led blockchain.

The cost of blockchain technology is a barrier to its adoption.

Blockchain brings higher consumer surplus and social welfare.

Choosing the proper selling format for online retail has long been a critical issue for many platforms to consider, whereas the emergence and popularity of live-streaming have had a massive impact on the platform's business. As a result, selecting the suitable operating strategy for the live channel has become another critical issue for platforms. In such a context, what will be the impact of live-streaming on selling formats?

In order to explore these issues, we identified two selling formats (wholesale reselling or agency selling) as well as two operating strategies (introduce or discard). Thereby, four channel-structures are constructed, namely the reselling-discard model (WN), the reselling-introduce model (WL), the agency-discard model (AN), and the agency-introduce model (AL). We comprehensively compare how different structures affect stakeholders' interests, consumer surplus, and social welfare through equilibrium analyses.

These results help clarify the impact of critical factors (e.g. self-effort attribute, cross-effort attribute, and commission ratio) on the choice of models. We find that regardless of the selling agreement between the manufacturer and the platform, the introduction of a live store is necessary; specifically, when the commission ratio is high, the platform's optimal decision is first to sign an agency agreement and then apply live selling (AL); conversely, when the commission ratio is low, the platform's optimal strategy is first to enable the live channel and then to select the reselling format (WL), together, this also reveals, from a theoretical perspective.

Our study includes the dual analysis of selling formats and channel operations, considering the inherent dual attributes of service efforts and the external competitive environment.

Demand forecasts are a key component of planning efforts and are crucial for managing core operations. This study aims to evaluate the use of several machine learning (ML) algorithms to forecast demand for residential construction in Jordan.

The identification and selection of variables and ML algorithms that are related to the demand for residential construction are indicated using a literature review. Feature selection was done by using a stepwise backward elimination. The developed algorithm’s accuracy has been demonstrated by comparing the ML predictions with real residual values and compared based on the coefficient of determination.

Nine economic indicators were selected to develop the demand models. Elastic-Net showed the highest accuracy of (0.838) versus artificial neural networkwith an accuracy of (0.727), followed by Eureqa with an accuracy of (0.715) and the Extra Trees with an accuracy of (0.703). According to the results of the best-performing model forecast, Jordan’s 2023 first-quarter demand for residential construction is anticipated to rise by 11.5% from the same quarter of the year 2022.

The results of this study extend to the existing body of knowledge through the identification of the most influential variables in the Jordanian residential construction industry. In addition, the models developed will enable users in the fields of construction engineering to make reliable demand forecasts while also assisting in effective financial decision-making.

The network capacity deployed to manage the busy hour (or peak-hour) traffic remains underused during the nonbusy (off-peak) hours. Transferring some traffic from peak-hour to off-peak hours is likely to improve the utilization of network resources during the off-peak hours. This paper aims to examine whether diverting traffic from peak-hour to off-peak hours is possible by adopting the differential pricing policy.

The peak-load pricing theory suggests that the policy of differential pricing is socially optimal when there is peak demand for a particular duration and then there is off-peak demand. In this study, hourly traffic data from both peak and off-peak periods were collected from the Indian telecom service provider, “Aircel.” The paper analyzed the disparity in traffic between peak and off-peak hours using the nonparametric Tukey’s test. An experiment was also conducted to analyze whether a significant shift in telecom traffic occurs from the peak to the off-peak period when a price discount is applied during the off-peak period.

Statistically significant differences were observed in network traffic between peak-hour and off-peak hours. The network utilization of the telecom service provider Aircel was notably lower, particularly during the off-peak hours. The experiment demonstrated a high degree of price sensitivity among telecom service subscribers. Telecom Regulatory Authority of India (TRAI) has not considered network utilization of telecom service providers as a key performance indicator. Based on the outcomes of the study, this paper recommends that TRAI should adopt a more proactive approach by encouraging telecom service providers to follow the policy of differential pricing to enhance utilization of their network capacity.

To the best of the authors’ knowledge, this is the first paper to explore the issue of pricing as a tool for bringing about more uniform movement of telecom traffic, thereby enhancing network utilization within India’s telecommunications sector.

The case has been developed by using secondary sources of information.

Tesla’s much-awaited foray into the burgeoning Indian electric vehicle (EV) marketplace had hit the “high import tariff” roadblock. Discussions ensued and finally, Elon Musk, the CEO of Tesla and the Indian Government found common ground. The moot point of Tesla’s entry mode was resolved. Musk announced Tesla’s plan to set up an EV supply chain and manufacturing facility in the host country. This case discusses factors affecting location decision, market entry modes and international corporate-level strategies. Tata Motors sold affordable cars and was miles ahead in the EV race in India. Musk had to align Tesla’s India strategy with the company’s global strategy to woo the price-sensitive Indian consumers. What were the options available to him? This case examines different business-level strategic options that could help Tesla drive in the fast lane in India.

The case can be used in international strategy course at graduate level. It can also be used in a session on international marketing in marketing management course.

This study aims to explore the structural changes in food consumption expenditure in Indonesia before and during the COVID-19 pandemic using data from the 2020 and 2021 National Socioeconomic Survey by Statistics Indonesia.

The quadratic almost-ideal demand system analysis model is used to estimate changes in the share of food consumption and the demand and price elasticity of food commodities in Indonesia. A total of 15 food items are analyzed to determine changes in food consumption expenditure during the COVID-19 pandemic.

The results of this study show that during the COVID-19 pandemic, there was an increase in the proportion of household expenditure related to the consumption of home-cooked food. Simultaneously, the proportion of expenditure on prepared food significantly decreased.

The pandemic has changed household food consumption in Indonesia. This study recommends that the government ensure the availability of supplies and stability of food prices and provide financial subsidies to maintain food consumption, especially for lower-income communities.

There has yet to be a study on the changes in household food consumption during the COVID-19 pandemic in Indonesia. Therefore, this research provides empirical evidence that there were changes in household food expenditure during the pandemic.

The study aims to investigate how attribution theory in the context of pricing strategies can help tourism destinations recover from the negative impacts of the COVID-19 pandemic.

The study adopted a qualitative research design using semi-structured interviews to address the lack of research in this area. Interview participants included tourists and tourism customers. The interview responses were then analyzed using “Nvivo” qualitative data analysis software to identify critical themes regarding applying attribution theory to pricing strategies.

The findings revealed that tourists prefer bundled and hedonic pricing strategies that integrate the service providers' pricing strategies' locus of control, stability and controllability. Tourists do not favor dual pricing strategies unless the reasons for price controllability or stability are justified. Tourists also prefer the controllable pay-what-you-want pricing strategy. Although tourists accept dynamic pricing, certain conditions related to price locus, stability and controllability must be met.

The research shows tourists prefer pricing strategies that give them control and flexibility, like bundled packages and pay-what-you-want models. Service providers should integrate pricing strategies that transparent costs and justify price fluctuations. While dynamic pricing is accepted if necessitated by external factors, tourists are wary of unnecessary price changes. Providers can build trust and satisfaction by explaining pricing rationale and offering controllable options like bundles.

The study contributes to the theory by applying attribution theory to the context of pricing strategies in tourism. It also provides innovative recommendations for tourism managers on how to use pricing strategies after the COVID-19 pandemic. The findings offer new insights that extend beyond previous research.

This paper aims to examine the impact of announcements related to 77 interventions by 46 listed Indian pharmaceutical firms during COVID-19 on the abnormal returns of the firms. The study also finds the variables which explain cumulative abnormal returns (CARs).

This study uses standard event methodology to compute the abnormal returns of firms announcing pharmaceutical interventions in 2020 and 2021. Besides this, the multilayer perceptron technique is applied to identify the variables that influence the CARs of the sample firms.

The results show the presence of abnormal returns of 0.64% one day before the announcement, indicating information leakage. The multilayer perceptron approach identifies five variables that explain the CARs of the sample companies, which are licensing_age, licensing_size, size, commercialization_age and approval_age.

The study contributes to the efficient market literature by revealing how firm-specific nonfinancial disclosures affect stock prices, especially in times of crisis like pandemics. Prior research focused on determining the effect of COVID-19 variables on abnormal returns. This is the first research to use artificial neural networks to determine which firm-specific variables and pharmaceutical interventions can influence CARs.