Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis

The main goal of this research is to provide a comprehensive understanding of the actual progresses in artificial intelligence, with emphasis on chatbots as emerging forms of customer assistance in online retailing. Drawing upon an analysis of the chatbot patents in the past 20 years, our findings show the increasing technology push towards the adoption of new conversational agents based on natural language. Findings also highlight the extent to which the research and development efforts are attempting to improve artificial intelligence systems that characterize chatbots. To this end, technology advancements are mainly focusing on: (i) improving chatbot ability to automatically draw inferences on users starting from multiple data sources, and (ii) using consumers’ knowledge adaptively to provide more customized solutions. Finally, results show the tight relationship between the digital assistants’ analytical skills and their ability to automatically interact with the users.     

Noise from online discussion forums: Evidence from IPOs in China

In this study, we use initial public offerings (IPOs) in China to investigate how online stock forums influence information asymmetry and IPO valuation. The empirical analysis isolates the underpricing and overvaluation components of initial returns. The number of forum comments, postings, and readings are positively associated with initial returns and the degree of underpricing, implying that forums create noise that exacerbates information asymmetry during IPOs. This effect is amplified by the quiet period regulation, which drives investors to rely on online discussion forums to obtain information. Through sentiment analyses of forum posts and media coverage, we find that the negative effect of online forums is more prominent when bad news prevails. We clarify the role of online stock forums in IPO pricing and information asymmetry by separating underpricing from overvaluation in initial returns.     

How social distance affects the intention and behavior of collaborative consumption: A study based on online car-hailing service

In recent years, advances in mobile communications technology have enabled collaborative consumption or product sharing between consumers on a large scale. Unlike traditional consumption, collaborative consumption is based on collaboration among individuals, so that the decision-making mechanisms of individual consumers may be different from those in traditional consumption scenarios. The current study focuses on how the social distance between consumers and drivers affects collaborative consumption intention in the case of online car-hailing services. In this study, the theory of planned behavior (TPB) is used as the foundational framework, and we innovatively add the concept of social distance to the TPB to form a new, and integrated model. We test the model based on data collected from 315 online car-hailing users. The results shows that behavioral attitudes, subjective norms, and perceived behavioral control, positively influence collaborative consumption intention and behavior. More interestingly, we find that social distance has both direct and indirect impacts on collaborative consumption intention: The greater the social distance, the lower the collaborative consumption intention. Moreover, social distance also moderates the influence of subjective norms and perceived behavioral control on collaborative consumption intention. To be specific, the influence of subjective norms and perceived behavioral control on collaborative consumption intention is weakened when consumers perceive less social distance. The results suggest that the integrated model has a stronger explanatory power on collaborative consumption behavior. This study enhances the traditional TPB model and offers insight into promoting collaborative consumption in the context of the sharing economy.     

Fear appeals and coping appeals for health product promotion: Impulsive purchasing or psychological distancing?

The prevalence of online health communities (OHCs) motivates health product companies to manipulate persuasion-based marketing strategies, including fear appeals and coping appeals, to promote health products. However, whether such a practice is effective remains unclear. Respondents may either impulsively purchase the recommended health products or simply distance themselves psychologically from the information. Building on protection motivation theory (PMT), this study uncovers the mechanisms behind these two coping responses to persuasive messages, namely, the impulsive purchasing and psychological distancing of OHC users. Correspondingly, we evaluated 366 valid questionnaires, and the results reveal that (1) when encountering fear appeals, perceived health threat can evoke cyberchondria, leading to psychological distancing; and (2) when encountering coping appeals, perceived response efficacy leads to product affection, fostering impulsive purchasing. This study provides new insights into the effects of persuasive messages for health product promotion in social commerce. Marketers should design appropriate persuasive messages to foster impulsive purchasing by generating individuals’ product affection and avoiding cyberchondria.     

Motivation and satisfaction of Chinese and U.S. tourists in restaurants: A cross-cultural text mining of online reviews

Tourists with dissimilar cultural backgrounds think and behave differently. Precisely capturing and correctly understanding the cultural difference will help tourism managers generate greater customer satisfaction and increased business revenue. To this end, this paper uncovers and compares the motivation and satisfaction of restaurant tourist customers coming from China and U.S. by investigating their online ratings and reviews. From two major online review communities, customer ratings and reviews have been retrieved, quantified, text-mined, compared, and interpreted using statistics, latent Dirichlet allocation, and frequency analysis. Results suggest that Chinese tourists are less inclined to assign lower ratings to restaurants, and are more strongly fascinated by the food offered, whereas U.S. tourists are more apt to be fun-seeking, and are less uncomfortable with crowdedness.     

Instructional Manipulation Checks: A longitudinal analysis with implications for MTurk

Instructional Manipulation Checks (IMCs) assess respondent behavior by, for example, asking participants not to answer a trick question. We find IMCs can be used to detect problematic response behavior in longitudinal surveys. This is important because a Latent Class Analysis based on IMC-failure in the two reported studies reveals that between 9% and 12% of respondents can be labelled Inattentive Stayers at the first survey-wave and between 13% and 17% at the third wave. The tendency of Inattentive Stayers to remain in the panel is particularly relevant for online panel services such as MTurk, with workers participating in many surveys over longer time-periods. We find IMC-failure to be mitigated by a warning to respondents that their attention will be checked early in the survey, but not by repeated exposure to IMCs, or a timer leaving questions on the screen longer. Respondent personality also plays a role in IMC-failure, which implies persistency of undesirable respondent behavior.     

It makes me feel vulnerable! The impact of public self-disclosure on online complaint behavior

Genuine reviews are essential for financial performance and for customer trust. In the case of a negative experience, withholding negative reviews may lead to biased online information. This paper aims to investigate two factors that may prompt customers to withhold their complaints (i.e., identity disclosure and locus of control). The study adopts a sequential exploratory mixed-method approach. An exploratory interview phase compares public vs anonymous online environments, and explores the reasons why dissatisfied customers might decide not to complain. An experiment then shows that vulnerability is a key mechanism preventing negative reviews. This effect is exaggerated when customers perceive that they do not have control over the events happening in their lives (i.e., external locus of control). Our work uncovers an important determinant of online complaint behavior, vulnerability. It also offers practical suggestions, such as improving confidentiality, to increase a customer's willingness to complain following a negative experience.     

Big data hedonic pricing: Econometric insights into room rates’ determinants by hotel category

This research uses a big dataset of online prices published on Booking.com by three-, four- and five-star hotels located in Madrid (Spain). Data is broadened by other sources, resulting in a rich set of context-, hotel- and offer-based variables. This research aims to determine the impact of these variables on the online room prices set by one representative sample, featuring the total pricing behaviour as well as per hotel categories. The variables considered, their extent and the insight per category represent a novelty and complement the literature on demand forecasting and hedonic pricing, enabling the improvement of optimisation techniques. The models, based on regression analysis with random effects, reveal a significant impact of the variables on room prices and a clearly distinct behaviour for five-star hotels. This research contributes to the scientific community and practitioners by showing the pricing dynamics behaviour of an important urban destination by star rating.     

Are bidders in auctions anchored by their bidder number?

This article examines whether bidders in a school fundraising auction were anchored by their bidder number. We find no evidence that they were.     

Predicting bid prices by using machine learning methods

It is well-known that empirical analysis suffers from multicollinearity and high dimensionality. In particular, this is much more severe in an empirical study of itemized bids in highway procurement auctions. To overcome this obstacle, this article employs the regularized linear regression for the estimation of a more precise interval for project winning bids. The approach is put to the test using empirical data of highway procurement auctions in Vermont. In our empirical analysis, we first choose a set of crucial tasks that determine a bidder’s bid amounts by using the random forest variable selection method. Given the selected tasks, project bid forecasting is conducted. We compare our proposed methodology with the least square linear model based on the bias and the standard root mean square error of the bid estimates. There is evidence supporting that the suggested approach provides superior forecasts for an interval of winning bids over the competing model. As far as we know, this article is the first attempt to provide reference bids of highway construction contracts.