Large volumes of product reviews generated by online users have important strategic value for product development. Prior studies often focus on the influence of reviews on customers‘ purchasing decisions through the word-of-mouth effect. However, little is known about how product developers respond to these reviews. This study adopts a big data analytical approach to investigate the impact of online customer reviews on customer agility and subsequently product performance. We develop a singular value decomposition-based semantic keyword similarity method to quantify customer agility using large-scale customer review texts and product release notes. Using a mobile app data set with over 3 million online reviews, our empirical study finds that review volume has a curvilinear relationship with customer agility. Furthermore, customer agility has a curvilinear relationship with product performance. Our study contributes to innovation literature by demonstrating the influence of firms‘ capability of utilizing online customer reviews and its impact on product performance. It also helps reconcile inconsistencies found in literature regarding the relationships among the three constructs.

Online peer-to-peer (P2P) lending, one of the most successful technology-enabled initiatives in the fintech revolution, has drastically changed the way individual investors and borrowers meet and transact. While prior research has found herding among investors at the listing level, such social behavior has been underexplored at a macro, platform level. In this study, we attempt to fill this gap by examining whether subsequent investors follow their predecessors’ actions when choosing which platform to invest, and if so, how various platform attributes and regulations moderate herding behavior. We collected a novel data set from leading platforms in a large P2P lending market. Our baseline analysis reveals that herding exists at the platform level. Using a multilevel model, we further identify several interesting moderators: the investor’s herding behavior is accentuated by platforms’ market share and the cumulative amount funded, but attenuated by their time in operation. Finally, we find that government regulatory events dampen the magnitude of the herding effect, suggesting that more information disclosure and stricter operation standards reduce the value of observational learning. The results from our analysis provide implications for P2P lending investors, platform designers, and policymakers.

Purpose: The purpose of this paper is to develop a model to examine how different technological and cognitive cues related to mobile food ordering applications (MFOAs) impact diners' intentions to use MFOAs continuously. The moderating role of national household demographics was also assessed in the online-to-offline (O2O) food delivery services (FDS) context.Design/methodology/approach: Drawing insights from the uses and gratification (U&G) theory, an online sample survey of 968 valid respondents who had ordered or purchased food through delivery apps during the COVID-19 outbreak in China was used for the analysis. The proposed model was empirically tested using structural equation modeling.Findings: The results revealed that cues in MFOAs (i.e. ease of use, convenience, price saving orientation, the listing of various food choices, exploring restaurant patterns, and compatibility) directly influenced diners' e-satisfaction and that intention to use continuously is triggered by e-satisfaction during the COVID-19 crisis. Moreover, this study found that collectivist household diners emphasized ease of use, convenience, and compatibility. Comparatively, individualistic household diners placed more importance upon price saving orientation and listing of various food choices during the pandemic.Originality/value: The authors offer a new insight into customers' dining choices by extending the scope of O2O services in the food industry. The findings contribute to understanding diners' purchase decision-making processes regarding MFOAs' O2O-FDS during the COVID-19 crisis. The multi-group comparison via national household demographics also provides a new perspective regarding different dining preferences toward O2O-FDS.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Online medical consultation, which has been adopted by patients in many countries, requires a large number of doctors to provide services. However, no study has provided an overall picture of the doctors who provide online services. Objective: This study sought to examine doctors’ participation in medical consultation practice in an online consultation platform. This paper aims at addressing the following questions: 1) Which doctors provide online consultation services? 2) How many patients do the doctors get online? 3) What price do they charge? We further explore the development of market segments in various departments and various provinces. Methods: This study explores the dataset including all doctors providing consultation services in their spare time on a Chinese online consultation platform – Haodf. We also brought in statistics for doctors' providing offline consultations in China. To examine the stated research questions, we make use of descriptive statistics and comparative analysis. Results: There are 88,308 doctors providing online consultation in their spare time on Haodf, accounting for 5.3% of all doctors in China on September 23, 2017. Of these online doctors, 49.9% are high-quality doctors having a title of Chief Physician or Associate Chief Physician, and 84.8% come from the top, Level 3, hospitals. Online doctors had an average workload of 0.38, with an online and offline ratio of 1:14. The average price of online consultation is 32.3 Yuan. The online ratios for the Cancer, Internal Medicine, Ophthalmology-Otorhinolaryngology, Psychiatry, Gynecology-Obstetrics-Pediatrics, Dermatology, Surgery and Traditional Chinese Medicine departments are 16.1%, 4.4%, 6.3%, 9.5%, 5.8%, 18.0%, 10.8% and 3.8%, respectively. The workloads for these departments are 0.18, 0.24, 0.47, 0.40, 0.60, 0.94, 0.31 and 0.36, respectively. The average quoted prices for online consultation in these departments are 33, 31, 40, 42, 35, 39, 33 and 23, respectively. Most provinces located in eastern China have more than 4000 doctors online. The online workloads for doctors from most provinces range from 0.3 to 0.4. In most provinces, doctors’ charges range from 20 to 30 Yuan. Conclusions: Quality doctors are more likely to provide online consultation services, get more patients and charge higher service fees in an online consultation platform. Policies and promotions could attract more doctors to provide online consultation. The online sub-market for the departments of Dermatology, Psychiatry and Gynecology-Obstetrics-Pediatrics developed better than other departments like Internal Medicine and Traditional Chinese Medicine. The result could be a reference for policy making to improve the medical system both online and offline. Since all the data used for analysis were from one single website, the data might be biased and might not be a representative national sample of China.

This research investigates whether and how predecessors’ usernames—as evaluated from a perspective of perceived anonymity—affect successors’ herding momentum through the varying extent of perceived source credibility. Using a unique data set collected from a leading debt-based crowdfunding platform, we classify lenders’ usernames as either anonymous or real-seeming, with the latter referring to usernames that seem to reveal one’s legal name. We find that successors demonstrate weaker herding momentum toward predecessors who are presented with real-seeming usernames than anonymous ones. This finding, which we attribute to a lower extent of perceived credibility resulting from a nonconforming behavior, challenges the conventional wisdom that considers anonymity a negative factor for source credibility. Further, we demonstrate the importance of risk-related factors, in that the uncovered positive effect of perceived anonymity on herding is accentuated in the early stage of the fundraising period. Our findings provide actionable insights for platform owners to utilize the user heterogeneity with respect to perceived anonymity and hence perceived credibility in herding. These findings are also informative for borrowers who desire to exert effort to encourage participation from the crowd.