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This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.

Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models.

The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels.

This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR.

This study produced a reliable, accurate forecasting model considering risk and competitor behavior.

This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times.

This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.

This study aims to explore the challenges faced by the tourism and hospitality industry following the COVID-19 pandemic and to propose effective strategies for recovery and resilience of this sector.

The study analysed the challenges encountered by the tourism and hospitality industry post-pandemic and identified key strategies for overcoming these challenges. The study utilised the modified Delphi method to finalise the challenges and employed the Best-Worst Method (BWM) to rank these challenges. Additionally, solution strategies are ranked using the Criteria Importance Through Intercriteria Correlation (CRITIC) method.

The study identified significant challenges faced by the tourism and hospitality industry, highlighting the lack of health and hygiene facilities as the foremost concern, followed by increased operational costs. Moreover, it revealed that attracting millennial travellers emerged as the top priority strategy to mitigate the impact of COVID-19 on this industry.

This research contributes to understanding the challenges faced by the tourism and hospitality industry in the wake of the COVID-19 pandemic. It offers valuable insights into practical strategies for recovery. The findings provide beneficial recommendations for policymakers aiming to revive and support these industries.

The study examines the instigating factors behind the development of the local content (LC) policy in Ghana and it further investigates the accountability mechanisms that drive the LC policy implementation to promote sustainable development.

The study reports on a series of interviews with key actors using Institutional Theory and the application of Bovens’ (2010) Global Accountability Framework as a lens for discussion and interpretation of results.

The results reveal that two forces instigated LC policy enactment. One is external funding pressure from the Norwegian government and the World Bank. The other is the government’s engagement of Civil Society Organisations and other internal stakeholders to justify its activities and missions to signal adherence to impartiality, neutrality, and, to a lesser extent, solidarity. The analysis also reveals tensions in how accountability legitimacy relates to implementation of the LC policy. The study further discovers that while participation, transparency, monitoring, and evaluation are frequently invoked as de jure institutional legitimacy in oil and gas contracts, actual practices follow normative (de facto) institutionalism rather than what the LC policy law provides.

The interview had a relatively small number of participants, which can be argued to affect the study’s validity. Nevertheless, given the data saturation effect and the breadth of the data obtained from the respondents, this study represents a significant advancement in LC policy enactment knowledge, implementation mechanisms and enforcement in an emerging O&G industry.

The findings of this study suggest that future policy development in emerging economies should involve detailed consultations to increase decision-maker knowledge, process transparency and expectations. This will improve implementation and reduce stakeholder tension, conflict and mistrust.

The findings of this study build on earlier investigations into legitimacy, accountability and impression management in and outside the O&G sector. Also, the findings reveal the legitimising tactics used by O&G actors to promote local content sustainable development targets.

This study aims to offer an overview of hospitality and tourism research on artificial intelligence (AI) and its impact on the industry. More specifically, this study examines hospitality and tourism AI research trends in hospitality and tourism customer service experience creation and delivery, service failure and recovery, human resources and organizational behavior. Based on the review, this study identifies the challenges and opportunities and provides directions for future studies.

A narrative synthesis approach was used to review the hospitality and tourism research on AI and its impact on various aspects of the industry.

AI and AI applications in customer service experience creation and delivery and its possible effects on employees and organizations are viewed as a double-edged sword. Although the use of AI and AI applications offers various benefits, there are also serious concerns over the ethical use of AI, the replacement of human employees by AI-powered devices, discomfort among customers and employees and trust toward AI.

The paper offers an updated holistic overview of AI and its implications in different facets of the hospitality and tourism industry. Challenges and opportunities are discussed to foster future discussions on the use of AI among scholars and industry professionals.

The food industry is continuously developing its online services called food delivery applications (FDAs). This study aims to evaluate FDA's importance–performance and identify strategies to maximize its potential gains from a business partner's perspective.

Data are collected from 208 FDA partners in Indonesia. Importance–performance analysis (IPA) is applied to evaluate the FDA feature and extended the theory of potential gain in customer value (PGCV) to achieve potential gains from FDA business partners.

This study provides a clear and measurable direction for future research to develop FDA performance. Owning customer data, revenue sharing and competitive advantage are the most potential gains from joining the FDA from the business partner perspective.

The respondents are restaurants from the micro, small, and medium enterprises levels. Further research should involve middle to upper level restaurants to discover all business partners' perceptions. This will be very helpful for FDA providers interested in improving the best performance for all their partners.

FDA providers must focus on improving and maintaining the features of owning customer data, revenue sharing, competitive advantage, stable terms and conditions, customer interface, building customer loyalty, online presence, user credit rating, promotion and offers, delivery service and sales enhancement to increase consumer satisfaction and meet the expectations desired by business partners.

This research provides a meaningful theoretical foundation for future work. It extends the theory of PGCV using the value of a partner perspective as a substitute for customer value; hence, the authors call it a potential gain in partner value.

This research tries to find the blockchain-based resilience strategies that can help the supply chains of micro, small, and medium-sized enterprises (MSMEs) to recover from the disruptions and work effectively in a resource-based view perspective.

Eight broad strategies and 32 sub-strategies are identified from the literature review. Delphi study was carried out, and detailed discussion with 16 experts helped in finalizing these strategies. Further, the best-worst method (BWM) prioritized these strategies.

The findings suggests that “building social capital,” improving “coordination capabilities,” “sensitivity towards market,” “flexibility in process and production,” “reduction in process and lead time,”and “having a resource efficiency and redundancy” are the top strategies on which the top management should focus to overcome the situations of disruptions and enhance performance of MSMEs.

The blockchain-based strategies will enable the companies in tracing the products from the company to customers. Further, the customers will be able to identify their manufacturers, the raw materials used in manufacturing, and the life and quality of raw used materials. Altogether the textile industry will become more sensitive toward environmental practices.

The previous research has not identified and evaluated the blockchain-based resilience strategies, and therefore this study tries to fill this gap. This study used a smaller sample from the experts, so the results may vary if the larger data set is used and hypothesis testing can be done.

In most economies, there are rules from the market regulators or government to sell at an equal wholesale price (EWP). But when one upstream channel is facing a negative demand disruption and another positive, EWP can create extra pressure on the disadvantageous supply chain partner, which faces negative disruption. The purpose of this study is to analyse the impact of EWP and the scope of the discriminatory wholesale price (DWP) during disruptions.

For the study, the authors used a dual-channel supply chain consisting of a manufacturer, online retailer (OR) and traditional brick-and-mortar (BM) retailer. Stackelberg game is used to model the interaction between the upstream and downstream channel partners, and the horizontal Nash game to analyse the interaction within downstream channel partners. For modelling asymmetric disruption, the authors took instances from the lock-down and post-lock-down periods of the COVID-19 pandemic, where consumers flow from BM retailer to OR store.

By analysing the disruption period, the authors found that this asymmetric disruption is detrimental to the BM channel, favourable to OR and has no impact on the manufacturer. But with DWP, the authors found that the profit of the BM channel and manufacturer can be increased during disruption. Though the profit of the OR decreased, it was found to be higher than in the pre-disruption period. Under DWP, the consumer surplus increased during disruption, making it favourable for the customers also. Thus, DWP can aid in creating a win-win strategy for all the supply chain partners during asymmetric disruption. Later as an extension to the study, the authors analysed the impact of the consumer transfer factor and found that it plays a crucial role in the optimal decisions of the channel partner during DWP.

Very scant literature analyses the intersection of DWP and disruptions. To the best of the authors’ knowledge, this study, for the first time uses DWP as a tool to help the disadvantageous supply chain partner during asymmetric disruptions. The study findings will assist the government, market regulators and manufacturers in revamping the wholesale pricing policies and strategies to help the disadvantageous supply chain partner during asymmetric disruption.

After reading this case study, the students will be able to identify the various choices available in social e-commerce using network marketing, interpret data-driven decisions in social e-commerce and evaluate their role in scaling business, analyse cost and revenue management in value segments, evaluate technology adoption among the masses using appropriate communication structures and develop customer relationships and manage their sentiments in the era of social media.

DealShare became a unicorn in 2022 and targeted the rural and low-income groups. Based on a networking model for customer acquisition and a hyperlocal supply chain model, DealShare is increasing its customer base at a rapid pace. However, profitability was still a challenge, and converting high volume into high value continued to be a daunting task. This case study delves deep into the challenges co-founder Sourjyendu Medda and the DealShare team faced. It seeks to address key issues: how should DealShare leverage customer network for faster customer acquisition and how should they increase ticket size and profitability? As a data-driven business, what advantages does DealShare have in influencing customers’ buying behaviour using data? Dependence on social media could have a cascading effect on “word of mouth”. How can they manage customer complaints and increase engagement?

This case study has the potential to be used in different settings. In strategic cost management, this case study can demonstrate strategies for cost management in the value-conscious segment. This case study can be used in marketing management courses while teaching “positioning” in business-to-consumer markets and CRM. For second-year management students, this can be used in entrepreneurship and strategic management courses to demonstrate the network effect in social e-commerce start-up businesses. This case study is also relevant for various course modules in graduate management programmes to demonstrate the power of data-driven decision-making in business.

Teaching notes are available for educators only.

CSS 8: Marketing

There has been a lot of research on how to set marketing budgets, but the overlooked aspect was how allocating funds influences business performance in a multi-goal context. This study aims to examine the relationship between business performance, the process of allocating funds to multiple goals and the interaction among the goals.

Ratio data were generated through “a constant sum scale” from a sample of 362 managers from the B2C sector, besides data on after-tax revenue for two years. The data file was created. Then, a factor analysis was performed on the data. Furthermore, an econometric model with interaction terms was fitted to the data.

The results show that allocating funds to multiple marketing goals – demand generation, customer experience, brand image, marketing competency and purchase intention – influences business performance. Furthermore, a goal’s impact on business performance is higher when coupled with other goals than in isolation.

The findings of the study should assist managers in increasing revenue while spending less on marketing and shifting funds from less efficient goals and pairs of goals to highly efficient ones.

By extending the relevant theory on the relationship between the process of marketing fund allocation, multiple goals and business performance, this study contributes to the literature on marketing.

The importance of service firms cooperating with digital platforms is widely acknowledged. The authors study three contractual relationships (fixed-cost, cost-sharing, and profit-sharing) between service firms (specifically hotels) and digital platforms in a highly fragmented service supply chain to examine which of these contract types optimizes profits.

The authors extend prior models analyzing the optimal expected total profit from the travel service firm (hotel)–digital platform relationship, providing new insights into each contract type’s ability to coordinate decentralized systems and optimize profits for both parties.

This study finds that fixed cost contracts cannot coordinate the decentralized system. Cost-sharing contracts can coordinate the decentralized system but only allow one channel profit split. In contrast, profit-sharing contracts may not always perfectly coordinate the decentralized system but support alternative profit allocations. Practically, both profit-sharing and cost-sharing contracts are preferable to fixed-cost contracts.

The paper includes implications for travel service firm managers to consider when structuring contracts with digital platforms to focus on profit optimization. Profit-sharing contracts are most preferable when cost and revenue data are fully shared between parties, while cost-sharing contracts are preferable over fixed-cost contracts.

This study extends prior investigations into the utility of different contract types on the optimal profit of a travel service firm (hotel)-digital platform provider relationship. The research fills a gap in the literature concerning the contracts used in these relationship types.