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The beauty products and cosmetics sector in India has grown rapidly due to shifting customer preferences and a greater focus on personal grooming. With the continued growth in sales of beauty products, it is critical for firms looking to succeed in this cutthroat industry to comprehend the dynamics of customer ratings and how they affect the quality of their products. Firstly, this study identifies major trends, obstacles, and possibilities influencing the nexus between artificial intelligence and the Indian beauty goods market by utilizing the body of current literature and empirical data. Secondary data is used in this research, two Kaggle datasets--Amazon Beauty Product Sales Rating and Amazon Beauty Product Recommendation--are analyzed in this study. We seek to provide insight on the complex relationship between customer ratings and product quality and how these aspects affect the success of beauty goods in the Indian market by contrasting these datasets. The objective of this study is to stimulate advancements in the domain of beauty product sales in India by establishing a connection between theoretical research and operational implementations. In the conclusion, our summary of the literature seeks to guide future initiatives focused at leveraging AI to promote innovation, growth, and sustainability in the ever-changing Indian beauty goods sector. The research's conclusions have ramifications for Indian consumers and producers of cosmetic products. This study has results for Indian consumers as well as cosmetic product manufacturers. Customer feedback may provide manufacturers with strategic insights to improve the quality of their products, and customers can use it to make well-informed decisions that suit their tastes. In the end, this research adds to the larger conversation on the dynamics of consumer happiness and product quality in India's thriving beauty and cosmetics industry. [ABSTRACT FROM AUTHOR]

Purpose: Academic research has supported the belief that consumers undertip minority race service workers due to implicit racial biases. However, there has been less focus in examining possible moderating factors. This paper aims to fill this gap by analyzing the role of direct and indirect experience in tipping frontline service workers from a minority background. Given the prominence of customer ratings on digital service platforms and the perception that African Americans are discriminated against, the authors look at the interplay of interaction length (direct experience) and customer ratings (indirect experience) on the relationship between race and tipping. Design/methodology/approach: An expectancy disconfirmation framework was developed and tested with a sample of 360 US participants in an online experiment. The experiment followed a 2 × (race: African-American versus Caucasian) × 2 (direct experience: limited versus extensive) × 3 (indirect experience: absent versus positive versus negative customer rating) design. Findings: The authors found consumers who have extended direct experience (longer service interaction) and no indirect experience (absent customer ratings) tipped African Americans more than Caucasians. Interestingly, this effect is reduced in the presence of indirect experience (customer ratings). Finally, where the consumer lacks direct experience (shorter service interaction) but is exposed to positive indirect experience (positive customer ratings), consumers tip African Americans more. Originality/value: To the best of the authors' knowledge, this is the first paper that examines the role of direct and indirect experience in the relationship between race and tipping. Based on the authors' findings, the authors provide several contributions, including recommendations to reduce inequalities arising from implicit racial bias on digital service platforms. [ABSTRACT FROM AUTHOR]

This study investigates how variance and average rating as a combination factor play different roles in the decision-making process by constructing a game-theory model. Results show that a higher variance is incorporated with low quality in frequently-purchased products and unreliable quality in infrequently-purchased products when average product ratings are high. A higher variance has a stronger influence on consumer demand for frequently-purchased products than on infrequently-purchased products when both products receive similarly high average ratings. There is a counter-effect when both products receive similarly low average ratings. The study demonstrates how consumers are keen to make a risky purchase decision preferring low quality products with a higher variance when making decision on two products that are substitutes for each other with the same average rating. This study will assist managers in developing marketing communication strategies to reduce pre-purchase buyers’ feelings of uncertainty. [ABSTRACT FROM AUTHOR]

This paper presents a novel method that uses online ratings to predict business failures. The success, if not survival, of service businesses depends on their ability to satisfy their customers. Yet, businesses often recognize slumping customer satisfaction too late and ultimately fail. To prevent this, marketers require early warning tools. In this paper, we build upon online ratings as a direct measure of customer satisfaction and, based on this, predict business failures. Specifically, we develop a variable-duration hidden Markov model; it models the rating sequence of a service business in order to predict the likelihood of failure. Using 64,887 ratings from 921 restaurants, we find that our model detects business failures with a balanced accuracy of 78.02%, and this prediction is even possible several months in advance. In comparison, simple metrics from practice have limited ability in predicting business failures; for instance, the mean rating yields a balanced accuracy of only around 50%. Furthermore, our model recovers a latent state ("at risk") with an elevated failure rate. Avoiding the at-risk state is associated with a reduction in the failure rate of more than 41.41%. Our research thus entails direct managerial implications: we assist marketers in monitoring customer satisfaction and, for this purpose, offer a data-driven tool that provides early warnings of impending business failures. [ABSTRACT FROM AUTHOR]

By means of a quasi-experimental study, the effects of a tailor-made job crafting intervention for employees of a Dutch unemployment agency were evaluated. The intervention was designed to prevent a decrease in employee empowerment, work engagement and employee performance (i.e., the provision of services) due to organizational changes. Seventy-four employees received a 1-day training in which they set four job crafting goals for the subsequent weeks. After 6 weeks a reflection session was organized. Repeated measures ANOVA’s showed that the intervention prevented a decrease in employees’ feelings of empowerment. Furthermore, pre-post comparison tests showed that the control group (N = 89) experienced a significant decrease in work engagement, whereas the intervention group did not. Results showed no effect on customer-rated employee service quality. However, 1 year after the intervention, customer ratings of employee service quality were significantly higher for the intervention group compared to the control group. Although further research is needed, our results demonstrate that a job crafting intervention may be a promising tool to combat a decline in employee empowerment and work engagement during times of organizational change.

The importance of customer ratings or reviews in online shopping has been recognized in the previous literature; however, few have studied how online customer rating scores affect consumers' fresh produce purchases and its importance relative to other fresh produce attributes. The quality of fresh produce demonstrates high uncertainty and variation; therefore, the impact of user‐generated content such as customer rating scores on the choice of fresh produce may be more complex than on other product categories. Moreover, previous studies on customer ratings have not examined the price premium that retailers can obtain based on better ratings of fresh produce. Using a stated preference approach (i.e., choice experiment), this study measures the willingness to pay for a higher customer rating score and explores its relative importance to other popular fresh produce attributes (i.e., organic, place of origin, and shelf life). The results show that customer rating is the second most important attribute after the place of origin and is more important than production methods (e.g., organic and naturally grown) for fresh strawberry purchases. Also, rating scores demonstrate a diminishing marginal impact on consumer willingness to pay. Younger consumers and households with children are willing to pay more for fresh produce with high ratings than those with low ratings. [ABSTRACT FROM AUTHOR]

By means of a quasi-experimental study, the effects of a tailor-made job crafting intervention for employees of a Dutch unemployment agency were evaluated. The intervention was designed to prevent a decrease in employee empowerment, work engagement and employee performance (i.e., the provision of services) due to organizational changes. Seventy-four employees received a 1-day training in which they set four job crafting goals for the subsequent weeks. After 6 weeks a reflection session was organized. Repeated measures ANOVA's showed that the intervention prevented a decrease in employees' feelings of empowerment. Furthermore, pre-post comparison tests showed that the control group (N = 89) experienced a significant decrease in work engagement, whereas the intervention group did not. Results showed no effect on customer-rated employee service quality. However, 1 year after the intervention, customer ratings of employee service quality were significantly higher for the intervention group compared to the control group. Although further research is needed, our results demonstrate that a job crafting intervention may be a promising tool to combat a decline in employee empowerment and work engagement during times of organizational change. [ABSTRACT FROM AUTHOR]

This study examines the impacts of online review features on hotel Online Room Sales for various types of hotel segments utilizing a dataset that includes 227,378 post-purchase customer review comments for 1,092 hotels. Data were analyzed by conducting three different analyses utilizing the log-log regression model. Findings suggest that overall recommendation scores and hotel attribute information are the two most critical features of online reviews used by consumers in their decision-making process to make their hotel booking decisions. Thus, changes in customer rating scores result in a profound impact on room sales. However, the impact sizes of these review features are not significantly different among different hotel segments, with one exception, the room rate. This study found that the impact size of the room rate is significantly larger for luxury hotels compared to other hotel segments. Managerial implications and contributions of the current study are discussed. [ABSTRACT FROM AUTHOR]

This study aims to establish a relationship between customer sentiments in online reviews and customer ratings for hotels. Customer sentiment refers to the emotions expressed by customers through the text reviews. These sentiments can be positive, negative or neutral. The study explores customer sentiments and expresses them in terms of customer sentiment polarity. Our results find consistency between customer ratings and actual customer feelings across hotels belonging to the two categories of premium and budget. Customer sentiment polarity explains significant variation in customer ratings across both the hotel categories. With regard to managerial implications, the study finds that, when compared with premium hotels, managers of budget hotels should improve their staff performance and hotel services. The present study is not exhaustive and other factors like customer review length and review title sentiment can be analyzed for their effects on customer ratings. [ABSTRACT FROM AUTHOR]

Merchants, as well as customers, have noticed the importance of online product reviews and numeric ratings in electronic commerce websites. It is valuable if merchants can discover some potential customer value from the sheer volume of data. This paper contributes a semantic text analytics approach that can dig out the customers' most basic concerns about their online purchase choices. More specifically, based on the hypothesis that the product reviews and overall ratings estimated by same person in a tiny time interval have a great relevance, we dexterously utilize this relevance to realize the embedded customer value. In the proposed method, take the single lens reflex camera for example, an innovative aspect extraction method that comprehensively considers the product ontology and results of the topic modeling method latent Dirichlet allocation is applied. As a result, 8 specific aspects are identified from the experimental results. For each aspect, a self-contained review feature corpus is created as an extension of some seed terms. After aspect-based sentence segmentation and context-sensitive sentiments preprocessing, aspect-oriented sentiment analysis is applied. Multiple regression analysis is then used as a statistical measure to discover determinant aspects of overall ratings. The results reveal that cost performance, image quality and product integrity are the three most influential aspects. The practical implication of our research is that merchants can efficiently modify their products, to satisfy more customers and also boost sales performance. [ABSTRACT FROM AUTHOR]

In this article, we analyze the impact on hotels of the ISO 14001 environmental certification system from the customers’ perspective. Based on a comparison of customer ratings of 6,850 hotels in Spain with and without ISO 14001 certification, overall guests rate the hotels with ISO 14001 certification higher than those without the certification. These results are stronger for hotel comfort and hotel services compared with other hotel attributes. Moreover, the most significant differences were found in the upscale four-star hotels. While the study does not reveal causes for these findings, the implication is that the highest end five-star luxury hotels do not gain distinctive differentiation by having the ISO 14001 certification, while for three-star hotels, guests’ price sensitivity overrides environmental concerns. At the four-star level, however, hotels seem to be able to gain a distinct market advantage from environmental certification. For all hotels, the management discipline provided by ISO 14001 can provide a competitive advantage. [ABSTRACT FROM AUTHOR]

This paper explores whether and how a firm should adapt its strategy in view of consumer use of prior customer ratings. Specifically, we consider optimal pricing and whether the firm should offer an unexpected frill to early customers to enhance their product experiences. We show that if price history is unobserved by consumers, a forward-looking firm should always modify its strategy from single-period optimal one, but it may be optimal to do so by lowering price, by lowering price and offering frills, or by raising price and offering frills, depending on the market growth rate. Specifically, the last strategy becomes optimal when market growth rate is high enough. The results are similar when the price history is observed by consumers, except that no deviation from single-period profit maximization choices is optimal when market growth is low enough. We also analyze whether the firm should prefer that the price information be stated in or left out of consumer reviews. In addition, in considering the effects of consumer heterogeneity, we conclude that the optimal firm's effort to affect ratings is higher when the idiosyncratic part of consumer uncertainty is larger. [ABSTRACT FROM AUTHOR]

The hotel market has become extremely competitive over the past years. Hotels try to differentiate themselves through their services and facilities. To make the best choice when searching for accommodation, guests increasingly use rating systems of booking sites. Using an ordered logit model (OLM), we identify, in our study, a sample that comprises of 635 hotels from Romania. These are the hotel facilities that significantly influence customer review scores (as an expression of customer satisfaction) on booking.com, the most widespread rating system. We also identify whether their impact on intervals of satisfaction levels vary. Some explanatory variables invalidate the Brant test for proportional odds assumption. Thus, for the final estimates, we use a generalized ordered logit model (GOLOGIT). The results show that food-related facilities, restaurants, and complimentary breakfasts, are very significant for customer ratings. Relevant hotel common facilities are the pool and parking spaces, while for the room—the flat-screen TV. It is interesting to note the negative influence of pets, which seem to disturb other tourists. In the sustainability category, only facilities for disabled people and electric vehicle charging stations are relevant. [ABSTRACT FROM AUTHOR]