Indiana University-Purdue University Indianapolis (IUPUI) / The aim of the study is to assess the impact of robot service on hotel guest experiences. Application of technology in tourism and hospitality services is growing each day. Using robots in hospitality establishment is becoming more and more popular, mainly because it can help cut down the labor costs, increase efficiency and reduce human contacts. Very few studies, however, have been done on examining customer experience regarding robots used in the hotel. Social media sites such as TripAdvisor are popular platforms where people share their first-hand experiences. Hence, this study focuses on studying the reviews of robotic hotels. Using the software Leximancer, reviews were studied and categorized in different themes to understand if the presence of the robot would create positive or negative experience for customers. The sample of the study included total of 2383 reviews related to robotic hotels from TripAdvisor from January 2011 to October 2020. The findings highlighted the major themes as Room, Robot, Hotel and Staff and their relationship with the ratings. It also provided insights into the contribution of robot service to consumer’s hotel experiences. / 2021-12-01

During the last decade online retail sales have been growing constantly. This growth has been possible due to different factors like online reviews. Online reviews have been proven successful in predicting different variables like trust and sales in online settings; however, the impact of online reviews on other variables like customer satisfaction has not been widely studied. Based on expectation-disconfirmation theory, this study analyzes the effect of online reviews on customer satisfaction. A set of six hypotheses were proposed and tested by using a controlled experiment. A total of 278 usable responses were obtained from a sample of college students from a major Midwest US university. Five of the six hypotheses were supported. Results indicated that expectations and perceived performance are significant predictors of disconfirmation. They also indicate that disconfirmation is a significant predictor of satisfaction. Regarding the effect of online reviews on satisfaction, results showed an indirect effect of online reviews on satisfaction mediated by expectations and disconfirmation. Results have implications for research and practice. For research, results help to increase the understanding of customer expectations formation in online settings. For practice, results give advice to sellers about how to increase customer satisfaction.

Previous research has shown how venting one’s feelings can reduce the negative emotions of a consumption experience. This study proposes a general process of how consumers with feelings of unfairness due to a negative consumption experience can achieve emotional disclosure and reduced unfairness by posting online reviews. By using an experimental design with scenarios, this study tests how the perceived unfairness in this process is affected by the party encouraging the consumer to post an online review and the consumer’s public commitment. A student sample was divided into four groups and the perception of unfairness was compared between the groups depending on whether the party encouraging the feedback was a company perceived to be responsible for the sense of unfairness or an independent party and whether when the consumer was identifiable or anonymous to see if public commitment had an effect. Results showed that emotional disclosure was found to reduce the perceived unfairness in all groups. There was no significant difference between being encouraged by the company or independent party. Furthermore, no public commitment was in effect, even when participants’ answers were thought to become known to others. The results indicate that companies may prefer to encourage consumers to provide feedback themselves rather than using a third party and that posting online reviews will not make the consumer committed to their feeling of unfairness.

Abstract Introduction Word-of-mouth (WOM) is based on personal recommendations where the sender is known by the consumer, thus, the persuasive nature of WOM is attributed to trust between the sender and the receiver of a message. Electronic word-of-mouth (eWOM) however, eliminates the consumer’s ability to judge the credibility of sender and message. Nevertheless, a high amount of people read online reviews about products and therefore make use of eWOM. Online reviews can be anonymous or can offer additional personal details of the sender and can have an influence on the credibility of the message, which in turn, can induce different attitudes towards specific products. Purpose This study aims to identify as well as understand the relationship between anonymous and semi anonymous eWOM and its corresponding characteristics in regards to the attitudes of consumers towards a laptop computer. Methodology A qualitative research method was conducted with the intention to understand the relationship between anonymous and semi anonymous eWOM towards consumers attitude. Primary data was collected, as the authors of this study were not able to locate research studies concerning the difference between anonymous and semi-anonymous eWOM and its relationship towards consumer’s attitudes. For this reason, four focus groups were carried out with students from the Jönköping University. During a pilot study, differences between male and female participants became visible therefore the focus groups were separated between men and women with the intention of collecting significant data. Conclusion The research was successful as it led to identify a relationship between the personal attributes of an online reviewer and the consumer attitudes towards a laptop. By reading online reviews and thus, observing the opinion of other people as well as using comparisons of different laptops, consumers form attitudes towards laptops. Moreover, it appears that consumers’ attitudes are more likely to be influenced by the message if it is perceived as credible. Several personal attributes of a reviewer such as name, photograph of a person, pseudonym, age, gender, country of residence and profession were identified to have an influence on the credibility of a message, whether they might increase or decrease the credibility. Additionally, it became visible, that women are relatively more likely to be influenced by personal attributes of a reviewer than men.

We present a novel marketing method for consumer trend detection from online user generated content, which is motivated by the gap identified in the market research literature. The existing approaches for trend analysis generally base on rating of trends by industry experts through survey questionnaires, interviews, or similar. These methods proved to be inherently costly and often suffer from bias. Our approach is based on the use of information extraction techniques for identification of trends in large aggregations of social media data. It is cost-effective method that reduces the possibility of errors associated with the design of the sample and the research instrument. The effectiveness of the approach is demonstrated in the experiment performed on restaurant review data. The accuracy of the results is at the level of current approaches for both, information extraction and market research.

The purpose of this dissertation intends to discover as well as categorize safety concern reports in online reviews by using key terms prevalent in sub-categories of safety concerns. This dissertation extends the literature of semi-automatic text classification methodology in monitoring and classifying product quality and service concerns. We develop various text classification methods for finding key concerns across a diverse set of product and service categories. Additionally, we generalize our results by testing the performance of our methodologies on online reviews collected from two different data sources (Amazon product reviews and Facebook hospital service reviews). Stakeholders such as product designers and safety regulators can use the semi-automatic classification procedure to subcategorize safety concerns by injury type and narrative type (Chapter 1). We enhance the text classification approach by proposing a Risk Assessment Model for quality management (QM) professionals, safety regulators, and product designers to allow them to estimate overall risk level of specific products by analyzing consumer-generated content in online reviews (Chapter 2). Monitoring and prioritizing the hazard risk levels of products will help the stakeholders to make appropriate actions on mitigating the risk of product safety. Lastly, the text classification approach discovers and ranks aspects of services that predict overall user satisfaction (Chapter 3). The key service terms are beneficial for healthcare providers to rapidly trace specific service concerns for improving the hospital services. / Doctor of Philosophy / This dissertation extends past studies by examining safety surveillance of online reviews. We examine online reviews reporting specific categories of safety concerns and contrast them with reviews not reporting these specific safety concerns. Business and regulators are benefited in detecting, categorizing, and prioritizing safety concerns across product categories. We use key terms prevalent in domain-related safety concerns for granular analysis of consumer reviews. Secondly, beyond utilizing the key terms to discover specific hazard incidents, safety regulators and manufacturers may use the extended risk assessment framework to estimate the risk severity, risk likelihood, and overall risk level of a specific product. The model could be useful for product safety practitioners in product risk identification and mitigation. Finally, this dissertation identifies the aspects of service quality concerns present in online hospital reviews. This study uses text analytics method by using key terms to detect these specific service concerns and hence determine primary rationales for patient feedback on hospital services. Managerially, this information helps to prioritize the areas in greatest need of improvement of hospital services. Additionally, generating key terms for a particular service attribute aids health care policy makers and providers in rapidly monitoring specific concerns and adjusting policies or resources to better serve patient

In recent years, the expansion of online media has presented firms with rich and voluminous new datasets with profound business applications. Among these, online reviews provide nuanced details on consumers' interactions with products. Analysis of these reviews has enormous potential, but the enormity of the data and the nature of unstructured text make mining these insights challenging and time-consuming. This paper presents three studies examining this problem and suggesting techniques for automated extraction of vital insights. The first study examines the problem of identifying mentions of safety hazards in online reviews. Discussions of hazards may have profound importance for firms and regulators as they seek to protect consumers. However, as most online reviews do not pertain to safety hazards, identifying this small portion of reviews is a challenging problem. Much of the literature in this domain focuses on selecting "smoke terms," or specific words and phrases closely associated with the mentions of safety hazards. We first examine and evaluate prior techniques to identify these reviews, which incorporate substantial human opinion in curating smoke terms and thus vary in their effectiveness. We propose a new automated method that utilizes a heuristic to curate smoke terms, and we find that this method is far more efficient than the human-driven techniques. Finally, we incorporate consumers' star ratings in our analysis, further improving prediction of safety hazard-related discussions. The second study examines the identification of consumer-sourced innovation ideas and opportunities from online reviews. We build upon a widely-accepted attribute mapping framework from the entrepreneurship literature for evaluating and comparing product attributes. We first adapt this framework for use in the analysis of online reviews. Then, we develop analytical techniques based on smoke terms for automated identification of innovation opportunities mentioned in online reviews. These techniques can be used to profile products as to attributes that affect or have the potential to affect their competitive standing. In collaboration with a large countertop appliances manufacturer, we assess and validate the usefulness of these suggestions, tying together the theoretical value of the attribute mapping framework and the practical value of identifying innovation-related discussions in online reviews. The third study addresses safety hazard monitoring for use cases in which a higher yield of safety hazards detected is desirable. We note a trade-off between the efficiency of hazard techniques described in the first study and the depth of such techniques, as a high proportion of identified records refer to true hazards, but several important hazards may be undetected. We suggest several techniques for handling this trade-off, including alternate objective functions for heuristics and fuzzy term matching, which improve the total yield. We examine the efficacy of each of these techniques and contrast their merits with past techniques. Finally, we test the capability of these methods to generalize to online reviews across different product categories. / Doctor of Philosophy / This dissertation presents three studies that utilize text analytic methods to analyze and derive insights from online reviews. The first study aims to detect distinctive words and phrases particularly prevalent in online reviews that describe safety hazards. This study proposes algorithmic and heuristic methods for identifying words and phrases that are especially common in these reviews, allowing for an automated process to prioritize these reviews for practitioners more efficiently. The second study extends these methods for use in detecting mentions of product innovation opportunities in online reviews. We show that these techniques can used to profile products based on attributes that differentiate them from competition or have the potential to do so in the future. Additionally, we validate that product managers find this attribute profiling useful to their innovation processes. Finally, the third study examines automated safety hazard monitoring for situations in which the yield or total number of safety hazards detected is an important consideration in addition to efficiency. We propose a variety of new techniques for handling these situations and contrast them with the techniques used in prior studies. Lastly, we test these methods across diverse product categories.

Due to the rapid development of Internet communication technologies (ICTs), an increasing number of social media platforms exist where consumers can exchange comments online about products and services that businesses offer. The existing literature has demonstrated that online user-generated content can significantly influence consumer behavior and increase sales. However, its impact on organizational operations has been primarily focused on marketing, with other areas understudied. Hence, there is a pressing need to design a research framework that explores the impact of online user-generated content on important organizational operations such as product innovation, customer relationship management, and operations management. Research efforts in this dissertation center on exploring the co-creation value of online consumer reviews, where consumers' demands influence firms' decision-making. The dissertation is composed of three studies. The first study finds empirical evidence that quality signals in online product reviews are predictors of the timing of firms' incremental innovation. Guided by the product differentiation theory, the second study examines how companies' innovation and marketing differentiation strategies influence app performance. The last study proposes a novel text analytics framework to discover different information types from user reviews. The research contributes theoretical and practical insights to consumer-centric innovation and social media analytics literature. / PHD

Thesis advisor: Michael Grubb / Online reviews have gained importance for consumers when shopping for experience goods. This dissertation documents the impact of Tripadvisor.com reviews on the hotel industry. In the first chapter, I investigate the causal impact of Tripadvisor review ratings on hotel performance via a regression discontinuity design. The results indicate that a 1-point increase in review rating leads to a 1.6% increase in revenue, a 1% increase in bookings, and a 0.4% to 0.6% increase in prices. Furthermore, the impact on bookings has increased over time. In the second chapter, I evaluate the welfare impact of Tripadvisor review ratings in providing information about quality. I develop a structural model of hotel demand and supply that takes price endogeneity and capacity constraints into consideration. Counterfactual experiments reveal that the removal of Tripadvisor from the status quo results in per-capita consumer surplus loss ranging from $0 to $5.8, with a more significant decrease in consumer surplus when prior knowledge about quality is less accurate. Hotels with higher quality than expected absent reviews benefit from review ratings, while the opposite is true for others. In the third chapter, I analyze the relative influence of Tripadvisor ratings on chain-affiliated and independent hotels and evaluate the value of Tripadvisor ratings compared to chain brands using the methodology developed in previous chapters. I find there is no significant difference in the effect of rating rounding on occupancy rates for chain-affiliated hotels versus independent hotels. Counterfactual experiment results suggest that despite chain brands providing value to consumers, Tripadvisor ratings provide additional value of about $0 to $4 per capita. In scenarios where Tripadvisor was not present, Chain-affiliated hotels benefit from brand affiliation while independent hotels are harmed. / Thesis (PhD) — Boston College, 2023. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

Through four essays, this dissertation contributes to the body of marketing literature by advancing understanding of consumer interactions with digital decision aids. Different aspects of the content contained within digital decision aids are explored in several contexts. First, the drivers of consumer interactivity in an online review community are examined and it is found that violations of community norms are an important factor in stimulating consumer action. Second, a tool is developed to facilitate the normalization of online review content across languages. Next, elements of language and national culture are investigated to determine their influence on consumer reviews in an international context. It is found that cultural biases play an important role in the relative verbosity, valence, and helpfulness of online reviews across countries. Lastly, the role of images in digital decision aids is considered and it is found that image type and perspective can influence consumer product evaluation. In sum, the influence that content and context have on consumer interactions with digital decision aids is clearly demonstrated through a diverse yet intertwined set of studies. / Business Administration/Marketing