Template-Type: ReDIF-Article 1.0 Author-Name: Girish K. Nair Author-X-Name-First: Girish K. Author-X-Name-Last: Nair Title: Data driven pricing strategies for hotels during the COVID-19 pandemic Abstract: Pandemics such as COVID-19 can challenge many of the very well-established strategies of revenue management. The purpose of this paper is to subject the four widely used strategies of revenue management in the hotel industry into an empirical analysis for their suitability at the time of crisis. The data driven approach to modelling has been adopted in this research based on the real-life data obtained from the property management system of nine of the 5-star hotels in Qatar. The results indicate that four of these hypotheses are supported. This revelation has been the basis for drawing implications to the finance and marketing managers of hotels to enhance customer satisfaction and maximise hotel revenue during the COVID-19 or any such pandemic. The results are based on real-life data and have the foundation of well-established pricing strategies in use at the hotels. Journal: Int. J. of Revenue Management Pages: 34-55 Issue: 1/2 Volume: 12 Year: 2021 Keywords: revenue management; pricing strategies; five-star hotels; COVID-19. File-URL: http://www.inderscience.com/link.php?id=114966 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijrevm:v:12:y:2021:i:1/2:p:34-55 Template-Type: ReDIF-Article 1.0 Author-Name: Mahartha Titi Author-X-Name-First: Mahartha Author-X-Name-Last: Titi Author-Name: Ubud Salim Author-X-Name-First: Ubud Author-X-Name-Last: Salim Author-Name: Sumiati Author-X-Name-First: Author-X-Name-Last: Sumiati Author-Name: Risna Wijayanti Author-X-Name-First: Risna Author-X-Name-Last: Wijayanti Title: Indonesian mutual funds: performance determinants and interaction of macroeconomic factors Abstract: We examine the determinants of fund returns of equity, fixed income, mixed, and money market mutual funds in Indonesia. Our empirical findings suggest that previous fund performances are significant determinants of current fund performances. On the other hand, fund age, management fee, and management period of the investment manager are not significant determinants of fund performances. Fund size is a significant determinant of fund performances only for equity funds. Furthermore, introducing the interest rate as a moderator variable weakens the effect of fund size on fund returns only for equity funds and it weakens the effect of the exchange rate on fund performances for equity and money market funds. For fixed income and mixed funds, the moderator variable does not change the effects of fund size and the exchange rate on fund performances. Overall, the robustness tests using three partitions of fund performances, namely bottom 20%, middle 60%, and top 20%, confirm our main findings. An interesting result is that returns of middle performers of fixed income funds are more sensitive to the changes of determinants than those of bottom and top performers. Journal: Int. J. of Revenue Management Pages: 83-103 Issue: 1/2 Volume: 12 Year: 2021 Keywords: mutual funds; fund characteristics; fund performances; Indonesia. File-URL: http://www.inderscience.com/link.php?id=114967 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijrevm:v:12:y:2021:i:1/2:p:83-103 Template-Type: ReDIF-Article 1.0 Author-Name: Wray Bradley Author-X-Name-First: Wray Author-X-Name-Last: Bradley Author-Name: Wen-Chyuan Chiang Author-X-Name-First: Wen-Chyuan Author-X-Name-Last: Chiang Author-Name: Li Sun Author-X-Name-First: Li Author-X-Name-Last: Sun Title: Unverifiable net assets ratio and annual report reading difficulty Abstract: The Financial Accounting Standards Board (FASB) has greatly expanded the use of fair value instruments, which amplifies an increase of unverifiable assets and liabilities that do not have actively traded market prices. Prior research indicates that managers may have strong incentives to use discretion in estimating fair values of such assets and liabilities for his/her self-serving interests, leading to more agency conflicts. This study examines the impact of unverifiable net assets on reading difficulty of annual reports. We predict that annual reports of firms with more unverifiable assets and liabilities are more difficult to read because prior research links agency conflicts to increased reading difficulty. Our results show a significant positive relation between unverifiable net assets and reading difficulty, consistent with the agency theory. Our study has implications for revenue management because prior research links earnings management to annual report readability. Journal: Int. J. of Revenue Management Pages: 104-131 Issue: 1/2 Volume: 12 Year: 2021 Keywords: unverifiable assets and liabilities; fair value; readability; annual reports. File-URL: http://www.inderscience.com/link.php?id=114968 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijrevm:v:12:y:2021:i:1/2:p:104-131 Template-Type: ReDIF-Article 1.0 Author-Name: Jiefeng Xu Author-X-Name-First: Jiefeng Author-X-Name-Last: Xu Author-Name: Evren Gul Author-X-Name-First: Evren Author-X-Name-Last: Gul Author-Name: Alvin Lim Author-X-Name-First: Alvin Author-X-Name-Last: Lim Title: Maximising store revenues using Tabu search for floor space optimisation Abstract: Floor space optimisation (FSO) is a critical revenue management problem commonly encountered by today's retailers. It maximises store revenue by optimally allocating floor space to product categories which are assigned to their most appropriate planograms. We formulate the problem as a connected multi-choice knapsack problem with an additional global constraint and propose a Tabu search-based metaheuristic that exploits the multiple special neighbourhood structures. We also incorporate a mechanism to determine how to combine the multiple neighbourhood moves. A candidate list strategy based on learning from prior search history is also employed to improve the search quality. The results of computational testing with a set of test problems show that our Tabu search heuristic can solve all problems within a reasonable amount of time. Analyses of individual contributions of relevant components of the algorithm were conducted with computational experiments. Journal: Int. J. of Revenue Management Pages: 56-82 Issue: 1/2 Volume: 12 Year: 2021 Keywords: floor space optimisation; FSO; revenue management; mathematical optimisation; meta-heuristics; Tabu search. File-URL: http://www.inderscience.com/link.php?id=114969 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijrevm:v:12:y:2021:i:1/2:p:56-82 Template-Type: ReDIF-Article 1.0 Author-Name: Neda Etebari Alamdari Author-X-Name-First: Neda Etebari Author-X-Name-Last: Alamdari Author-Name: Miguel F. Anjos Author-X-Name-First: Miguel F. Author-X-Name-Last: Anjos Author-Name: Gilles Savard Author-X-Name-First: Gilles Author-X-Name-Last: Savard Title: Application of machine learning techniques in railway demand forecasting Abstract: Demand forecasting lies at the heart of any revenue management system. It aims to estimate the quantity of a product or service that will be purchased in the future. In this paper, we perform railway demand forecasting for a major European railroad company by taking various contributing parameters into account. To have multipurpose results, the current problem is explored in two different aggregation levels. At the high level, the problem is defined as prediction of the total number of bookings for all trains departing on a specific departure date and within a certain time range. Moreover, in a more disaggregated level, the prediction models aim to compute the total number of bookings within each booking period for all trains leaving in a specific time range of a certain departure date. Using state-of-the-art machine learning methods and various heuristic feature construction techniques, remarkable results with high forecast accuracy and reasonable computational complexity are achieved in both aggregation levels. This paper aims to contribute to the application of ML techniques in RM by introducing new heuristic feature engineering techniques, exploring the importance of accurate clustering, and implementing state-of-the-art machine learning methods in the context of railway industry. Journal: Int. J. of Revenue Management Pages: 132-151 Issue: 1/2 Volume: 12 Year: 2021 Keywords: revenue management; demand forecasting; feature engineering; machine learning. File-URL: http://www.inderscience.com/link.php?id=114970 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijrevm:v:12:y:2021:i:1/2:p:132-151 Template-Type: ReDIF-Article 1.0 Author-Name: Santosh Kumar Author-X-Name-First: Santosh Author-X-Name-Last: Kumar Author-Name: Mrinalini Pandey Author-X-Name-First: Mrinalini Author-X-Name-Last: Pandey Title: 9-ending prices in retail advertisements: Indian consumers' price perception and proneness to buy Abstract: This study examines the effect of the ubiquitous use of 9-ending prices in newspaper retail advertisements by mall retail stores on Indian consumers' price perception and proneness to buy. In order to collect data, a mall-intercept technique was used. The data were analysed using multivariate statistical techniques. Findings reveal that the use of 9-ending prices in advertisements, convenience benefit, enhanced value, discount price, and misleading action positively impact Indian consumers' price perception. However, entertainment benefit, value expression benefit, savings benefit, and low-quality do not significantly impact the Indian consumers' price perception. The Indian consumers' price perception towards the advertisements of 9-ending prices in newspapers and free home-drop advertising materials has a positive impact on their proneness to buy. Journal: Int. J. of Revenue Management Pages: 1-33 Issue: 1/2 Volume: 12 Year: 2021 Keywords: 9-ending prices; retailing; consumers' price perception; CPP; consumers' proneness to buy; CPTB; advertising. File-URL: http://www.inderscience.com/link.php?id=114971 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijrevm:v:12:y:2021:i:1/2:p:1-33