The evaluation of thermal hotels' online reviews

Th e main objective of this study was to evaluate the perceptions related to the online user reviews of thermal hotels. Specifi cally, it was investigated whether perceptions towards value (V), location (L), sleep quality (SQ), rooms (R), cleanliness (C), service (S) and factors infl uencing general evaluation depend on the star numbers of hotels, the location of the hotels and the nationalities of participants. In order to obtain data on perceptions of consumers towards thermal hotels in Turkey, the web site Trip Advisor (TA) was used. In total, 2,895 user reviews about thermal accommodations on TA were assessed by content analysis method. According to the study results, it was determined that the most important factor was the cleanliness of the hotels. It was followed by the location, sleep quality, rooms and service. Th e value factor was the last important. To analyse the eff ect of the nationality of the participants, domestic and foreign visitors, stars and the location of the accommodation on the perceptions towards value, location, sleep quality, rooms, cleanliness and service, t test and one-way ANOVA method were performed. It was found that the perceptions towards value, location, sleep quality, rooms, cleanliness and service diff ered between domestic and foreign visitors, nationalities, location and 4 or 5-star

Application of Natural Language Processing to Determine User Satisfaction in Public Services

Research on customer satisfaction has increased substantially in recent years. However, the relative importance and relationships between different determinants of satisfaction remains uncertain. Moreover, quantitative studies to date tend to test for significance of pre-determined factors thought to have an influence with no scalable means to identify other causes of user satisfaction. The gaps in knowledge make it difficult to use available knowledge on user preference for public service improvement. Meanwhile, digital technology development has enabled new methods to collect user feedback, for example through online forums where users can comment freely on their experience. New tools are needed to analyze large volumes of such feedback. Use of topic models is proposed as a feasible solution to aggregate open-ended user opinions that can be easily deployed in the public sector. Generated insights can contribute to a more inclusive decision-making process in public service provision. This novel methodological approach is applied to a case of service reviews of publicly-funded primary care practices in England. Findings from the analysis of 145,000 reviews covering almost 7,700 primary care centers indicate that the quality of interactions with staff and bureaucratic exigencies are the key issues driving user satisfaction across England

Assessing regional digital competence: Digital futures and strategic planning implications

Understanding strategic decisions aimed at addressing regional economic issues is of increasing interest among scholars and policy makers today. Thus, studies that proffer effective strategies to address digital futures concerns from social and policy perspectives are timely. In light of this, this research uses strengths, weaknesses, opportunities and threats (SWOT) analysis tool to frame a regional strategy for digital futures to enhance place-specific digital connectivity and socio-economic progress. Focus group discussions and a structured questionnaire were conducted to examine a SWOT for a digital economy strategy in the Southern Downs Region in Queensland, Australia. The findings show that while the proposed regional strategies for digital futures are susceptible to internal and external forces, strategic planning makes them manageable. The study’s findings also reveal that adaptive strategic planning can help regulate the effects of internal and external factors that shape individual and organisational responses to digital transformation, and that these factors promote regional competitiveness

Integrating customer relationship management strategies in (B2C) e-commerce environments

Creating value and generating a total customer experience(TCE ) is important for E -Commerce in order to attract customers. However, with increasing competition in the marketplace, it is becoming increasingly difficult to retain customers. E-Commerce, therefore, should focus on continuously providing value to the customers to build long-term relationships and reduce customer defections. We have evaluated five E -Banking sites from the UK finance industry and have shown that an organisation whose Web site is usable in HCI terms (that satisfies the Web Design heuristics / E -Commerce guidelines) might not always generate a TCE . It is important that along with usability heuristics, customer relationship management (CRM) strategies are integrated into the design of E-Commerce sites. In this paper, we have applied the service quality (SERVQUAL ) framework from the relationship marketing literature for deriving customer relationship enhancing heuristics which can be integrated into the design of E -Commerce environments

Incentives for Quality over Time – The Case of Facebook Applications

We study the market for applications on Facebook, the dominant platform for social networking and make use of a rule change by Facebook by which high-quality applications were rewarded with further opportunities to engage users. We find that the change led to quality being a more important driver of usage while sheer network size became less important. Further, we find that update frequency helps applications maintain higher usage, while generally usage of Facebook applications declines less rapidly with age

Do User-Generated Ratings Affect Hotel Valuations? – An Analysis of the Chicago Hotel Market

Today’s travelers are increasingly relying on aggregated online opinions to make purchase decisions. One of the sectors most impacted by these online reviews is the hospitality industry, where consumer review websites such as TripAdvisor, Expedia, and Bookings.com play a critical role in influencing consumer\u27s choice of hotel and the price they will pay for the room. Recently, there have been studies investigating the various aspects of user-generated online reviews and ratings. The purpose of this paper is to investigate the impact of user-generated ratings on hotel valuations. Our paper does this using regression analysis to study the impact of TripAdvisor user ratings on Occupancy Rates, Average Daily Rate (ADR), and the corresponding market value of the hotels. The research was carried out on 33 properties operating in luxury through economy market segments and located within Chicago, Illinois. The results indicate user-generated ratings positively influence Occupancy rates and ADR. The findings indicate a robust relationship between higher user-generated ratings and higher Occupancy rates and ADR suggesting a corresponding increase in market values. The study also reveal a strong positive correlation between Seasonality and ADR. The academic and managerial implications of this research along with future directions have also been discussed

The Actual Structure of eBay’s Feedback Mechanism and Early Evidence on the Effects of Recent Changes

eBay’s feedback mechanism is considered crucial to establishing and maintaining trust on the world’s largest trading platform. The effects of a user’s reputation on the probability of sale and on prices are at the center of a large number of studies. More recent theoretical work considers aspects of the mechanism itself. Yet, there is confusion amongst users about its exact institutional details, which also changed substantially in the last few months. An understanding of these details, and how the mechanism is perceived by users, is crucial for any assessment of the system. We provide a thorough description of the institutional setup of eBay’s feedback mechanism, including recent changes to it. Most importantly, buyers now have the possibility to leave additional, anonymous ratings on sellers on four different criteria. We discuss the implications of these changes and provide first descriptive evidence on their impact on rating behavior

Personality in Computational Advertising: A Benchmark

In the last decade, new ways of shopping online have increased the possibility of buying products and services more easily and faster than ever. In this new context, personality is a key determinant in the decision making of the consumer when shopping. A person’s buying choices are influenced by psychological factors like impulsiveness; indeed some consumers may be more susceptible to making impulse purchases than others. Since affective metadata are more closely related to the user’s experience than generic parameters, accurate predictions reveal important aspects of user’s attitudes, social life, including attitude of others and social identity. This work proposes a highly innovative research that uses a personality perspective to determine the unique associations among the consumer’s buying tendency and advert recommendations. In fact, the lack of a publicly available benchmark for computational advertising do not allow both the exploration of this intriguing research direction and the evaluation of recent algorithms. We present the ADS Dataset, a publicly available benchmark consisting of 300 real advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated by 120 unacquainted individuals, enriched with Big-Five users’ personality factors and 1,200 personal users’ pictures