Many firms have recently implemented the buy-online-and-pick-up-in-store (BOPS) strategy on store operations. This paper examines the impact of power structures on the decision of pricing and service level in an omnichannel supply chain with BOPS option. We investigate a two-stage omnichannel supply chain that consists of an online manufacturer and an offline retailer. The manufacturer produces products and adopts an online channel while the retailer sells products on both the traditional and the BOPS channels. Based on game theory analysis, the optimal retail prices in different distribution channels and the retailer’s optimal service quality in an omnichannel supply chain are derived under different power structures. Our results show that the more powerful retailer enjoys a higher profit while the dominant manufacturer may not benefit from its first-move advantage. No dominance among omnichannel supply chain members lead to the highest service level. Our analysis generates managerial insights into the interaction between firms and provides a guide for implementing the BOPS strategy in omnichannel retailing.
Despite many scholars have devoted to understanding the effects of manager response on review helpfulness, the mechanism of how the topic consistency between manager responses and corresponding reviews affect review helpfulness remains unclear, especially in different conditions of review emotional intensity. Based on the uncertainty reduction theory, a research model reflected the impacts of topic consistency on review helpfulness in different conditions of review emotional intensity was developed. The research model was tested by using a negative binomial regression method with 31,523 reviews collected from TripAdvisor. The results showed that topic consistency between manager responses and corresponding reviews positively affect review helpfulness. In addition, such positive impacts will be enhanced when the reviews’ positive emotional intensity is high, while these impacts will be weakened when the reviews’ negative emotional intensity is high. In terms of the negative discrete emotions, anxiety has a negative moderating effect on the relationship between topic consistency and review helpfulness.
This paper employs the scientific econometric analysis approach to review 705 academic publications related to Fintech from 2006 to 2021. The historical evolution, latest status and development trend of FinTech research are identified by co-authorship networks, co-citation networks and timeline evolution. CiteSpace software is applied to conduct the literature analysis. The results show that the scientometric analysis based on CiteSpace is a practical approach to review the development of financial technology. The rapidly increasing number of publications confirms the vitality of the FinTech field. China, the USA, the UK, Australia and South Korea are the most productive countries in the FinTech field. In contrast, the UK is the country with the highest degree of inter-country cooperation. The analysis results of citation bursts and timeline evolution on FinTech research provide the trends of FinTech research in discipline categories and keywords. Finally, four frontier research streams of FinTech are proposed.
Twitch users watched over 1.2 billion hours of streaming video in a single month in 2020, with the vast majority of these hours devoted to videogames. The most popular streamers who create this content are often powerful influencers in a rapidly growing industry, and many industries now see videogame influencer marketing as a key aspect of their marketing mix. However, while some streamers have amassed incredible popularity on Twitch, the factors that drive live-streaming viewership remain poorly understood. This study empirically examines a large population of Twitch streamers to explore this existing gap in the current research and explain how potential viewers make the decision to patronize a Twitch streamer. Using panel data on the actions and characteristics of Twitch streamers combined with other sources, the study identifies the heuristic cues most associated with successful Twitch streamers. Ultimately, the study identifies and evaluates multiple heuristics around Twitch content delivery practices, with significant implications for any live-streaming context.
In a highly volatile environment, i.e., the bottom of the pyramid (BOP) context, small and medium-sized enterprises (SMEs) can develop and adapt their resource bases through partner relations, extending the emerging theorization of dynamic capabilities. We explore the detailed nature and antecedents of these dynamic capabilities based on a multicase study by tracing the development of 6 BOP-oriented SMEs in the pharmaceuticals, animal husbandry, agricultural product processing, and distribution fields. The research was conducted between June 2019 and November 2020. First, our rich qualitative data indicate two distinct types of dynamic capabilities that are pivotal for short-term performance advantage and sustainable competitive advantage. Adaptation dynamic capability relates to activities of resource deployment and renewed operational capability, which is supported by local legitimacy and trust mechanisms, constructed by strong ties with noncommercial partners. Shaping dynamic capability relates to activities of path-creation and innovation capability, which is supported by the heterogeneous information and cost advantages, generated by weak ties with commercial partners. Second, SMEs give priority to the development of adaptation dynamic capability as the basis of shaping dynamic capability, while their sequencing explains how SMEs create, leverage, and enhance them over time.
This study is the first to examine the impact of FinTech firms on bank financial stability. Using a sample of 26 banks from an emerging market (Malaysia), over the period 2003–2018, we find that the development of FinTech firms over time increases bank financial stability. We uncover further evidence that FinTech firms’ impact on bank financial stability holds when we conduct sub-sample analyses by bank size, bank type (Islamic vis-à-vis conventional), and level of corporate governance. The results are robust to alternative model specifications, measures of financial stability, and FinTech.
This paper addresses the notion of electronic economics by examining financial technology (fintech) firms' performance and corporate governance quality, using data from the United States. The findings support our maintained assumption that due to the economics of electronic platforms perused by financial technology firms, these firms outperform firms from other industries (non-Fintech). The final sample comprises 1,712 company-year observations between 2010 and 2019 (pre-COVID-19). The evidence suggests that our corporate governance quality index, developed from Organisation of Economic Cooperation and Development (www.oecd.org/corporate/corporate-governance-factbook.htm, 2019) guidelines, accurately captures corporate governance quality in the United States, principally due to the inclusion of an anti-bribery policy indicator, in the index. The results suggest that this evidence is not merely an artefact due to the corporate governance quality index potentially capturing priced risk factors. Our findings reveal that fintech firms have superior corporate governance quality than non-fintech and that fintech firms place more reliance on internal versus external corporate governance mechanisms, vis-a-vis companies in other industries.
This study considers a manufacturer–retailer–streamer supply chain, in which the retailer first purchases products from a manufacturer and then sells them to consumers through a streamer. In the live streaming context, the retailer usually cooperates with the streamer by providing three different contracts: only a commission of the sale (OC), only a fixed fee (OF), and a commission of the sale and fixed fee (CF). Therefore, this study develops a theoretical model to investigate the effects of these three contracts on supply chain members’ optimal decisions and profits. The following results were obtained: (1) the retailer prefers an OC contract with a high-ability streamer, and the manufacturer benefits from this contract. Additionally, the manufacturer, retailer, and high-ability streamer can achieve a win–win–win outcome in certain cases. Furthermore, the retailer is willing to sign an OC contract with a low-ability streamer when the fixed fee of the OF contract is high. (2) The retailer prefers to cooperate with a low-ability streamer through an OF contract when the fixed fee is low. (3) The CF contract is the most profitable alternative for the retailer when the total commission rate is low and the fixed fee is medium.
Language test preparation books are essential for second language learners to pass a specific test. Although numerous books of this type have been published, yet little attention has been paid to different market demands between different areas. To address this issue, the present study used E-commerce data, in particular positive comments, retrieved from four online shopping platforms to compare the differences and similarities in market demand for Chinese language proficiency test preparation books between China and four English-speaking countries. The data were analyzed using a long short-term memory model using Python from the perspectives of major market demand, customer profile, functional expectations and non-functional expectations. Theoretical and practical implications were proposed based on the findings.
This paper investigates three common sales modes with live streaming commerce, including e-commerce platform mode, transferring mode and live streaming platform mode (abbreviated as E, T and L, respectively). Using game-theoretical method, we study how participants choose sales modes with product return. The findings show that for the seller and platforms, each mode may be the best, depending on basic net sales volume and channel rate. However, for the streamer, mode T will never be the best. Additionally, product quality in mode L is always higher than that in mode T, and higher than that in mode E when live streaming platform’s basic net sales volume is high. Finally, hybrid mode may generate higher profits for members, except for the live streaming platform; we also study the impacts of transferring loss, gift-giving function and streamer’s dual-purpose on results through extensions, checking the model robustness and deriving more managerial insights.
Small and medium enterprises (SMEs) in India are suffering from the long-standing challenges related to asymmetric information, high transaction costs, SMEs’ opacity and limited access to credit. Blockchain technology, which is still in its infancy in terms of adoption in India, can facilitate SMEs to counter these challenges. Fuelled by this motivation, the study aims to investigate the significant barriers to blockchain adoption in supply chain finance practices by Indian SMEs. Using fuzzy-analytic hierarchy process, sensitivity analysis, and fuzzy-decision-making trial and evaluation laboratory this paper identifies the blockchain barriers, prioritises them and examine their cause and effect relationships. The results of the study indicate that technology barriers are the most influential barriers that impede blockchain adoption. The findings will help the policymakers and practitioners to take suitable measures to overcome these barriers and fuel the adoption of blockchain in Indian SMEs.
This current research focuses on developing and testing a measurement model of website quality for Small and Medium-sized Enterprises (SMEs). This study applied Hinkin’s scale development process and involved 802 respondents in Indonesia through two successive data collection phases. The model development confirmed the SMEs-WebQ model as a robust measurement of SMEs’ website quality, containing 15 items across three dimensions: system quality, information quality, and service readiness. The replication study confirmed a gratifying nomological validity of the SMEs-WebQ model, whereby the three dimensions of the SMEs-WebQ model have significant positive effects (through brand awareness) on consumers’ purchase intention. This study theoretically contributes to the website quality literature and adds to the debate over the theoretical applicability of the present study’s underpinning theories (stimulus-organism-response and flow experience) in understanding online purchase intention. Practically, this study provides managerial recommendations to SMEs’ managers looking to improve their websites while ameliorating their competitiveness in the digital business era.
In the wake of ongoing challenges faced by the disruption of COVID-19, the current study attempts to investigate fintech growth during COVID-19 in the Middle East and North African (MENA) region. The study applies descriptive analysis, content analysis, and keyword selection criteria to segregate current challenges and future prospects of fintech growth in the MENA region. Our study comprises 250 research articles, web reports, news articles, opinion papers, and commentaries. The study reported privacy issues, cybercrimes, financial disruption and instability, exploitation of social norms and values, rising inequalities, and non-compliance of regulatory authorities as major challenges posed by fintech startups in the MENA region. On the contrary, the future prospects of FinTech growth in the MENA region are employment opportunities, decentralization, cost-effectiveness, financial outreach, networking, and breaking traditional financial biases. The study recommended multiple implications for policymakers, regulation bodies, countries in the MENA region, and fintech developers.
In recent years, more and more wealth management platforms have adopted Internet wealth management (IWM) services to attract users. Due to the intensive competition and low switching costs, it is essential for platforms to enhance users’ willingness to continue using IWM services. Comprehensively considering the role of network externalities and herding, this study identified the factors affecting users’ continuous intention of IWM service. The research model was tested using survey data collected from 637 respondents concerning their perceptions of IWM services. The results indicate that network externalities (network size, perceived complementarity, network strength) have significant impact on herding and perceived value, thus affecting continuance intention. Furthermore, herding and perceived value have a greater impact on continuance intention of users with low financial literacy than that of users with high financial literacy. This study could benefit wealth management platforms and researchers seeking to improve the retention rates of IWM users.
Acquiring a single sentiment score dependent on all the reviews will benefit the buyers and sellers in making the decision more accurately. The raw format of user-generated content lacks a legitimate language structure. It, therefore, acts as an obstacle for applying the Sentiment analysis task, which aims to predict the true emotion of a sentence by providing a score and its nature. This paper concentrates on obtaining a single sentiment score using a hybrid Long Short-Term Memory encoder–decoder model. This research uses the text normalization process to transform the sentences consisting of noise, appearing as incorrect grammar, abbreviations, freestyle, and typographical errors, into their canonical structure. The experimental outcomes confirm that the proposed hybrid model performs well in standardizing the raw E-commerce website review, enriched with hidden information and provided a single sentiment score influenced by all the review scores for the product.
The booming live streaming business has increased the number of consumer options for purchase channels, which has had a significant impact on firm sales models and management practices. This paper considers a multiechelon supply chain in which an upstream supplier can sell products through an online platform and a live streaming sales channel, and the online platform can choose to sign a reselling agreement or an agency selling agreement with the supplier. By constructing a Stackelberg game, we theoretically derive the equilibrium strategies of the supply chain members under different selling agreements in the presence of a live streaming sales channel. Specifically, we find that under the reselling agreement, the optimal retail price for the platform decreases with an increase in the commission rate, while the retail price for the live streaming channel always increases with an increase in the commission rate, and the relationship between the wholesale price and the commission rate depends on the ratio of the coefficient on the internet celebrity’s effort for the live streaming channel to that for the online platform. Under the agency selling agreement, there is a threshold in the agency fee. The impact of the commission rate on pricing strategy on one side of the threshold is opposite that on the other side. Furthermore, we numerically explore the impacts of the system parameters on the selection of the sales model, profit, and the decision-making of the supply chain members and identify the conditions under which agency selling and reselling are chosen when there is a live streaming sales channel. Interestingly, we find that the internet celebrity should not charge a commission rate that is too large; otherwise, it harms others without benefiting the celebrity. Moreover, when the supplier chooses to hire an internet celebrity to sell goods via live streaming, the supplier should choose a celebrity with either an extremely large following or with little influence but should not choose a celebrity with only moderate influence.
Generation Z consumers are characterized by social networking and instant decision-making. The influence of different forms of electronic word of mouth (eWOM) on the consumption decisions of generation Z consumers is quite different. Based on information adoption model (IAM) and information transmission theory, the paper established the influence mechanism model of eWOM information structures on generation Z consumers’ purchase intentions. A total of 815 valid questionnaires were collected for empirical test through a 2*2*2 between-subject situational experiment. The empirical results reveal that eWOM information structures influence generation Z consumers’ purchase intentions through user perception. Among them, the eWOM information of diversified form, composite type, and hybrid type structure has a higher effect on perception credibility and usefulness than the eWOM information of single form, one-way type, and single type structure. Consumers’ professional ability plays a partial role in moderating the influence of eWOM information structure on user perception.
Current situation in COVID-19 pandemic as well as the significant digital transformation , where the whole world is being forced to participate, are lead for a wide acceptance to use the mobile payments. The main objective for the current study is to focus on analysing the primary variable "intention to use" through the Apple Wallet mobile payment system "apple wallet app" in United Arab Emirates (UAE), in addition to defining a context and evaluating the various antecedents of its use. The main variables that addressed by the current study are ability to use (skilful-ness), perceived usefulness, convenience of the system, perceived risk and the primary variable that mentioned before was intention to use. To conduct the study, we invited 422 respondents to an online survey, and we have used a structural equation modelling analysis. The results indicate that mobile user skilfulness is the variable that most influences the intention to use the proposed payment system, followed by perceived usefulness and convenience of the system, while the perceived risk has a weak negative relationship with intention to use mobile payment via apple wallet app in the light of high Cybersecurity Index in the UAE.
Alibaba's annual online shopping carnival is well known for being one of the most successful promotion campaigns, during which marketers often deliver as many informational incentives and promotion activities as possible to inspire consumers' fanatical participation and purchases. Nonetheless, there is a dearth of studies that examined the effect of such rationality manipulation on consumers decision-making process using real-world behavioral evidence, which gives us an opportunity to make up for this research gap. Using a unique shopping log dataset generated by consumers on the Tmall platform, we regard the promotional activities release date as source of exogenous shock and conduct a regression discontinuity in time design to examine the change in consumers rationality degree during the carnival. The empirical results show that consumers tend to deal with more external cues and be more stick to their original options within a shorter decision cycle during the carnival, which indicates their decreasing rationality degrees and thus verifies the effectiveness of marketers’ rationality manipulation. Interestingly, we also found an in-group bias that such rationality manipulation has different influences on consumer subgroups of different genders and ages. Among them, of particular note is that the consumer group younger than 24 years old not only has the biggest gender difference within the group, but also has the biggest difference with other age groups. Findings emerged from this study will help marketers improve promotion effectiveness and deliver a rational allocation of information resources on the e-commerce platform.
With the popularization of digital finance in China, mobile payments have penetrated into all aspects of residents' daily life. However, few studies have examined the potential impact of mobile payments on people's happiness in China. Using the nationally representative data from the China Household Finance Survey (CHFS), this study adopts the ordered probit regression with endogenous treatment to adjust for possible endogeneity to assess the effect of mobile payments on residents' happiness. The results suggest an association between mobile payment usage and increases in happiness, which is supported by several robustness checks, such as using an alternative instrumental variable (IV), replacing the explained variable, and removing some extreme observations. In addition, we explore the mechanisms by which mobile payments affect residents' happiness from multiple perspectives. Positive mechanisms include promoting quality of life, reducing transaction costs, stimulating entrepreneurship, and increasing social interaction. However, as a non-cash payment method, mobile payments may also lead to over-consumption, which is detrimental to residents' happiness. Furthermore, the heterogeneous analysis shows inclusive attributes of mobile payments. We find mobile payments have a greater positive effect on happiness of some socially disadvantaged groups, such as elderly individuals, rural residents, the low-educated, and low-income households. These findings supplement the literature on online happiness and financial inclusion and refer to the possible negative impact of mobile payments. Therefore, it is necessary to actively promote mobile payments to benefit more socially vulnerable groups and prevent potential risks from over-consumption.
Conventional approaches for identifying domain experts focus only on their level of expertise and fail to consider their innovation potential. Thus, we propose a more comprehensive method by considering the types of innovation tasks and their corresponding knowledge domains. With a set of novel and effective metrics, the proposed method is able to assess the knowledge quality and innovation potential of each participating user. We evaluate our method with a real-world dataset collected from a popular online innovation community. The results indicate that the proposed method is efficient and scalable for contributory domain expert identification with different innovation tasks and different knowledge domains. This work expands expert identification research by providing both a new theoretical angle and new technical solution for quantifying the value of users.
This study investigated the determinants of digital music success in South Korea. We identified information sources and factors that may influence the consumption of digital music, including song and artist factors, record label and distributor influence factors, promotional media influence factors, and electronic word of mouth (e-WOM). The analysis was conducted using music download data and ranking chart data from a major music platform. First, we found that traditional promotional media, such as the number of television or radio exposures and appearances in an audition program, significantly affected the success of music. Second, regarding social promotional media factors, promotional videos on YouTube mainly affected short-term success, whereas Twitter mentions showed an increasing influence over time. Third, an artist with a long career positively affected a song's early success, while frequent song releases had a negative impact. Our results offer marketers insights into promoting digital music in this new era.
In recent years internet auctions have attracted much attention. This paper discusses the "last minute bidding" (sniping) phenomenon, first investigated by Roth and Ockenfels (American Economic Review 92, 1093-1103) in eBay, fixed-end, auctions. Unlike standard auctions where each offer is processed by the auctioneer due to system traffic, and connection time, in eBay auctions very late bids may be left unprocessed. In the paper we consider a simple two-players, two-periods sealed-bid auction model, inspired by eBay auctions, with private values and complete information. The main difference with respect to e-Bay auctions is given by the first period. Indeed, players can submit at most one bid per period. In the first stage bids are processed with certainty by the system while in the second stage, with positive probability, bids may not be processed due to system congestion. Unlike eBay we show how in this model last minute, that is second stage only, bidding may have low plausibility, as it can be a Nash equilibrium only under very specific circumstances and is never a unique best reply. Intuitively this is because the first stage is sealed-bid, with at most one offer per bidder, and players have no guarantee that they could outbid the opponent in the second period, once after the first round they realise that their bid is not the best.
The literature does not agree on the precise role of socio-demographic characteristics in the adoption of online grocery shopping. This methodological note reviews the literature and shows that the differences in empirical results can to a large extent be explained by the data that is used. In particular, what matters is whether or not the survey that is exploited was targeted at the household member primarily responsible for the grocery shopping. I show that studies that use a non-targeted survey erroneously find that women are keener to adopt e-grocery services, in that the gender gap is simply due to women’s role as homemakers. I also show that such studies tend to underestimate the impact of education and income.
Does intellectual capital positively affect the success of the equity crowdfunding campaigns? This paper answers this question using an original dataset of 191 equity crowdfunding campaigns, gathered from Italian platforms over the period 2014–2018; special attention is dedicated to patents effect, R&D and team’s education level on funding success, as they may serve as signals of unobservable quality of equity crowdfunding campaigns. Then, following literature on success factors for crowdfunding, the paper analyzes the signaling role of equity share retained by founders and by their social network size. Results suggest that intellectual capital (i.e. patents, R&D, team’s education level), amount of equity retained by projects’ founders and size of their social network have a positive and significant impact on fundraising success and are perceived as quality signals of crowdfunding campaigns by external investors.
To offer an appropriate recommendation to customers in recommender systems, the issue of clustering and separating users with different tastes from the rest of people is of significant importance. The MkMeans + + algorithm is a technique for clustering and separating users in collaborative filtering systems. This algorithm utilizes a specific procedure for selecting the initial centroids of the clusters and has a better function compared with its similar algorithms such as kMeans + + . In this paper, MkMeans + + algorithm is combined with Firefly, Cuckoo, and Krill algorithms and new algorithms called FireflyMkMeans + + , CuckooMkMeans + + , and KrillMkMeans + + are introduced in order to specify the optimal centroid of the cluster, better separate users, and avoid local optimals. In the proposed hybrid clustering approach, the initial population of firefly, cuckoo, and krill algorithms is initialized through the solutions generated by MkMeans + + algorithm, and it makes use of the benefits of MkMeans + + as well as firefly, cuckoo, and krill algorithms. Results and implementations on both MovieLens and FilmTrust datasets indicate that the proposed algorithms can perform better than their similar algorithms in clustering and separating users with different tastes (graysheep users), and enhance the quality of clusters and the accuracy of recommendations for users with similar tastes (white users).
This paper investigated the role of information related, social and customer characteristics in public information adoption tendencies of online customers to result in herding in e-commerce. E-commerce platforms contains numerous online reviews about products which have the potential to influence customers. We applied structural equation modeling and a 2 × 2 scenario experiment to empirically verify the effect of a few factors in creating online herding. Two levels of cognitive complexity (simple, complex) and risk aversion (risk averse, risk taker) formed the 2 × 2 factorial design. The study's primary finding was that a person with simple cognitive structure and risk avoidance tendency may exhibit higher intention to adopt public information and engage in herding. Information specific attributes contributed maximum towards information adoption and herding. Among sociological variables, only reputation concern significantly predicted both information adoption and herding. Theoretically, the study offered a framework to explore herding intentions online and augmented the observations from the information adoption model. The quality of concise information from credible sources significantly instigates adoption of public information contained in online reviews. From the perspective of marketers, having a better understanding of herding behaviors and its mechanisms can enable the e-commerce platform to reduce herding’s erosion on the wisdom of the crowd by optimizing its information structures (i.e., public information, private information, etc.).
The present study proposes a novel customer-to-virtual-product-to-customer (C2VP2C) mode of a loan default penalty model for Internet financial platforms (IFPs) in the Chinese market. The C2VP2C mode is developed based on the traditional peer-to-peer (P2P) business model and introduces IFP virtual products to risk control and loan matching. A loan default penalty model and a punishment mechanism of IFP borrowers in the C2VP2C mode have been developed. Firstly, the transaction mode and operational process of the C2VP2C mode of IFPs were established and three levels of loan matching space were constructed. The study established a penalty model for delinquent borrowers to assess their willingness to repay, and investigated the penalty intensity for defaults. The results show that a greater the penalty coefficient would result in more serious penalties, and with the delay of the repayment, the penalty coefficient showed less changes. The proposed method has important practical value and scientific significance for reducing the default rate of IFP borrowers and improving the loan repayment rate.
Microblogs have gained concerns from both academics and practitioners owing to their great potential for communication and diffusion. Brand managers can release posts containing photo, video, and hashtags besides a short text on microblog platforms. This study concentrates on the influence of explicit characteristics in brand post (i.e., photo, video, hashtags, and brand personality traits) on consumer engagement (i.e., number of likes, comments, and shares of brand post). Furthermore, we propose that brand prestige moderates the relationships between brand post characteristics and consumer engagement. We examine these hypotheses with a dataset that includes more than 250,000 brand posts across about 70 brands’ official accounts collected from the platform Sina Weibo, the largest and most popular microblog platform in China. Our research yields interesting findings that uncover the relationships among different characteristics of brand post and consumer engagement under different levels of brand prestige. We conclude the paper by highlighting its theoretical and practical contributions.
This paper tackles the problem of e-commerce thesauri alignment. It includes the definition of three alignment techniques which can be combined to increase the effectiveness and reduce the execution time. It also introduces a filtering technique to reduce the number of candidates returned to the final user. This work reports a set of evaluations that were lead with real-world data. Results show that the proposed techniques outperform schema, the state-of-the-art approach. They also drastically reduce the execution time, thus making them more usable in real-world applications.
We consider a platform providing free content for users and earning profit from the sale of advertising. The platform can collect and analyze personal data to customize advertisements for individual users. Customization may alleviate users’ aversion to advertising, but it may also raise privacy concerns. Considering the platform as a two-sided market with asymmetric externalities, we investigate the effects of privacy concerns on the platform’s optimal advertising pricing and data collection strategies. We find that it is always beneficial for the platform to collect and analyze personal data. When users attach less concerns on privacy, the price increases with the efficiency of efforts and decreases with the initial nuisance cost of advertisements to users. However, if users attach more concerns on privacy, the price decreases with the efficiency of efforts and increases with the initial nuisance cost of advertisements to users.
Despite the existence of research in the field of electronic reverse auctions (eRAs), there is still a limited understanding of the determinants of auction savings that exist in this process, especially factors that can change information asymmetry during auctions. In comparison with other studies, attempts have been made to test the effects of various levels of information asymmetry through the prolongation of auctions and through changes to minimum bid amounts on auction results, as well as other modifiable variables. More than 11,000 eRAs were analysedusing data from a leading auction platform in Central Europe. The application of the Mann–Whitney–Wilcoxon test on data divided by medians of analyzed variables has been confirmed as a valid method for verifying the significance of the developed conceptual model relationships. While confirming several relations indicated by laboratory experiments and other studies, several findings to the contrary of the expected relationships were also confirmed.
Short video excerpts from TV shows are a tool that producers/broadcasters use to promote their programs. This study examines how video highlights that are presented online for free viewing, which can be analogous to product samples for entertainment goods, affect TV audience ratings. We investigate whether a displacement effect exists, i.e., the substitution of goods due to the availability of other similar goods. We find that positive viewer response, measured by the number of likes and views generated for the highlights, positively affects ratings, and the square of the number of likes negatively affects ratings. Our findings suggest that if viewers are overly satisfied with the highlights, some may be satisfied with merely viewing them and refrain from watching the actual show; such a response may potentially decrease TV viewership. This is the first study to examine the role of online video highlights as a promotional tool for TV shows.
A large volume of customer reviews is generated from time to time and customer requirements are presented between lines of online opinions. Many studies about online opinions mainly focus on the extraction of customer sentiment, but practical concerns regarding the integration into new product design are far from extensively discussed. To enlighten designers about how consumers differ geographically in terms of their preferences, which is possessing important research significance and practical values, is not well investigated. Specifically, in this study, online reviews are invited to explore market regional heterogeneity. With identified product feature related subjective sentences from online reviews, a straightforward applied approach is to assume the ratio of the number of satisfied customers to the total number of customers as the expected percentage of satisfied customers across different regions. However, such frequency based approach becomes unreliable in case that the number of reviews do not distribute evenly. Accordingly, the Bayesian school of thought is utilized in which statistics of data-rich regions are invited to help to analyze that of data-poor regions. Then, a hierarchical Bayesian model is proposed and it assumes that the expected percentages of customer satisfaction in different regions follow a certain probability distribution. Finally, taking 9541 mobile phone online reviews on Amazon as an example, categories of experiments were conducted. It informs the significance to product designers about the value of online concerns on analyzing market regional heterogeneity and presents the effectiveness of the proposed approach in terms of discovering customer regional differences.
Few studies have investigated the mechanism underlying the connection between votes and review helpfulness. A within-subject experiment with a between-group factor of personality traits was adopted to measure participants’ neural response in the processing of votes regarding review helpfulness. The results showed that a larger feedback-related negativity (FRN) and smaller P300 were induced when their personal voting behavior was inconsistent with the relative majority votes. The results confirmed that the participants established a reference frame of relative majority votes via comparison of the votes associated with other visible reviews and that they applied this reference frame to evaluate their personal voting behavior. Moreover, the participants with higher openness showed lower ΔFRN values, confirming the individual differences in the influence of the reference frame of the relative majority votes on outcome evaluation.
The impact of place-of-origin on price premium for agricultural products in the online marketplace has received limited attention in the existing literature. This study draws from the elaboration likelihood model and investigates whether place-of-origin affects price premium. Moreover, this study explores how other cues [seller’s reputation, positive word-of-mouth (WOM) volume, and WOM valence] moderate the relationship between place-of-origin and price premium for agricultural products in the online marketplace. The result of the empirical study reveals that place-of-origin indeed has a significant and positive impact on price premium. Furthermore, the study finds a negative interactive effect between place-of-origin and other cues (seller’s reputation, positive WOM volume, and WOM valence) on the price premium for agricultural products in an e-commerce setting. The results highlight the importance of place-of-origin in the competitive online market and have implications both for academic research and for online retailing practice.
The emergence of cross-border e-commerce has brought new opportunities to traditional enterprises. This paper discusses the partner selection of cross-border e-commerce companies in the B2B mode. It constructs a theoretical model of partner selection of cross-border e-commerce enterprises based on literature review. Through the mathematical analysis of an asymmetric evolutionary game model, it is considered that the model has an evolutionarily stable strategy. Based on it, a multi-agent model is constructed. The results of the simulation reveal the mediation role of trust between corporate reputation and enterprise cooperation. Simultaneously, it verified the moderation effect of information sharing between the trust and cooperation of cross-border e-commerce companies. It also provides explanations for the inconsistency in the relationship between trust and cooperative behavior. From both mathematical and data perspectives, this paper attempts to test the theoretical model proposed, which enriches the methodology to test the theory.
Customer recognition provides an opportunity to the customers to think about services in service companies. Classifying customers into different categories based on their satisfaction helps these insurance companies to better manage their capital that results in more profit. Researchers have used different categorization methods to identify and classify customers based on their level of satisfaction with services. The purpose of this article is to present a new method for customer classification based on the satisfaction with services in the insurance company. It overcomes the inefficiencies of a classification method called Selectability/Rejectability Measures Approach for nominal classification and provides more accurate results. This method uses service quality criteria to better consideration of customers’ perceptions and expectations. Finally, a numerical example is provided to justify the proposed method. The input data is obtained from a survey in which 384 complete questionnaires collected from the customers are examined.
Electronic coupon (e-coupon) is one of the most important marketing tools in B2C e-commerce. To improve the e-coupon redemption rate and reduce marketing costs, it is crucial to retarget customers who have received e-coupons and have higher propensity to redeem their coupons. Using log data and transactional data to extract the features of past purchase behavior, past coupon redemption behavior and browsing behavior during coupon validity period, we investigate the factors influencing customers’ propensity for e-coupon redemption. Our results show that almost all the variables used in our analysis (except the visit time) affect consumers’ coupon redemption propensity. Our study can help companies develop promotional strategies that better retarget those customers who are more likely to respond to coupon marketing. It also highlights the potential of using predictive analytics to enhance marketing effectiveness in the era of big data.
The peer economy, such as crowdfunding, democratizes access to tasks available only to professionals. Although the peer economy has gained great popularity in practice, how crowds infer information from their peers, especially from experts, is still under minimal study in academia. Using data from a debt-based crowdfunding platform in China, this study investigates the impact of seasoned predecessors’ bids on subsequent investors' decisions and how seasoned and unseasoned investors respond differently to herding signals. We discover that the cumulative lending amount from seasoned predecessors is positively associated with the lending amount of a successor, and such an association is greater if the successor is seasoned. In the repayment process, we find that the lending amount from seasoned investors is positively associated with loan performance, while the lending amount from unseasoned investors is not. Our results contribute to the literature on crowds of wisdom, implying that in a context that requires sophisticated knowledge, extracting hidden talents from experts rather than from crowds is more appropriate.
Online reviews of a firm may come from diverse sources including real customers, competitors, and the firm itself. Review manipulation by posting fake negative reviews about competitors or fake positive reviews oneself has major impacts on product sales and firm reputation. This study aims to answer the question of whose reviews are most valuable for predicting a firm’s default risk. To uncover the value of manipulated and authentic reviews in firm default risk prediction, we conduct an empirical analysis using unique weekly panel data from a third-party portal of online peer-to-peer lending platforms in China. The results indicate that firm default probability increases with the number of manipulated positive reviews in the short term but decreases with the number of manipulated positive reviews posted over the long term. In addition, the signaling role of manipulated positive reviews is stronger when the peer-to-peer lending platform experiences more intense pressure such as downturn of business performance, stricter financial regulation policies, or aggressive attacks from competitors. Manipulated negative reviews are harmful for peer-to-peer lending platforms, which will increase the probability of platform default. Finally, authentic positive reviews are positively associated with default due to the overconfidence effect in the online lending context, and the authentic negative reviews in the short term work as a significant signal for fraud risk.
Blockchain operates on a highly secured framework, and its decentralized consensus has benefits for supply chain sustainability. Scholars have recognized the growing importance of sustainability in supply chains and studied the potential of blockchain for sustainable supply chain management. However, no study has taken stock of high-quality research in this area. To address this gap, this paper aims to provide a state-of-the-art overview of high-quality research on blockchain for sustainable supply chain management. To do so, this paper conducts a systematic literature review using a bibliometric analysis of 146 high-quality articles on blockchain for sustainable supply chain management that have been published in journals ranked “A*”, “A”, and “B” by the Australian Business Deans Council and retrieved from the Scopus database. In doing so, this paper unpacks the most prominent journals, authors, institutions, and countries that have contributed to three major themes in the field, namely blockchain for sustainable business activities, decision support systems using blockchain, and blockchain for intelligent transportation system. This paper also reveals the use of blockchain for sustainable supply chain management across four major sectors, namely food, healthcare, manufacturing, and infrastructure, and concludes with suggestions for future research in each sector.
This paper studies the effects of persuasive advertising and personalized pricing on duopolistic firms’ profits, consumer surplus and social welfare when one or two firms adopt consumer information to personalized pricing. With a game-theoretic model, the main results are summarized as follows: (1) The profits of both firms when they use consumer information are lower than those when neither of them use consumer information. (2) Consumer surplus increases with the number of firms who collect and use consumer information. Compared with the case that one or no firms have consumer information, the social welfare in the case that both firms have consumer information is the highest. (3) Given both firms adopt persuasive advertising simultaneously, the two firms will trap into “prisoner’s dilemma” when they decide whether to use consumer information or not. (4) In the duopolistic competition, the optimal strategy for data intermediary is to sell information to only one firm.
Motivated by the observed private-label products entering the market, this paper studies the encroachment of private-label manufacturers considering the quality effort and the platform’s e-word-of-mouth. We analytically derive that customer brand recognition ability and the e-word-of-mouth(e-WOM) gap of different platforms have an impact on the private-label manufacturer’s quality effort and price. Moreover, the higher the willingness to pay for the quality effort cost, the more effective the private-label encroaches market through platform channel. In addition, we find the weaker the customer's brand recognition ability, the higher the profit of the platform with good e-WOM in the face of the encroachment of private label through the platform with good e-WOM. However, in the face of the encroachment of private label through bad e-WOM and direct channel, the profit of platform with good e-WOM is lower. The e-WOM gap of the platform has a feeble impact on the profit of the brand manufacturer and platform. Furthermore, we find when customer brand recognition ability is high, the optimal encroachment channel for private label manufacturers is the platform with good e-WOM; when customer brand recognition ability is moderate, the optimal encroachment channel for private label manufacturers is the platform with bad e-WOM; when customer brand recognition ability is high, the optimal encroachment channel for private label manufacturers is direct channel.
A triple-band microstrip antenna based on the DGS technology is proposed with the enhancement of gain as well as improvement in bandwidth. Split Ring Resonator (SRR) is accomplished in the bottom plane which is working as a stop band in operating bandwidth. Two rectangular radiators are placed in series, where two symmetrical inverted L-shaped slots are responsible for bandwidth enhancement of the antenna. The 10 dB impedance bandwidth (IBW) of the antenna is 3.22 GHz to 3.35 GHz, 3.48 GHz to 3.58 GHz and 4.45 GHz to 4.59 GHz which can be used for 5G communication applications. The gain improvement is achieved in all operating bands and 5.2 dB maximum gain is observed at 3.23 GHz with 3.2 dB gain enhancement.
Food retailers are lagging behind other industries in implementing innovative mobile solutions offering their services and purchasing processes on their online platforms. Chatbots can be leveraged as an application to provide customer-centric services while retailers benefit from collecting consumer data. Previous literature on chatbot technology provides evidence that human characteristics enhance the customer experience. This is the first experimental study to investigate consumer attitudes and satisfaction with anthropomorphic chatbots in food e-commerce. A sample of 401 participants was tested to verify the proposed hypotheses. The test group interacted with a standard chatbot without human-like characteristics, while the control group communicated with the anthropomorphically designed agent. The results confirm the vast potential of anthropomorphic cues in chatbot applications and show that they are positively associated with customer satisfaction and mediated by the variables enjoyment, attitude, and trust. The findings suggest that to remain competitive, food retailers should immediately adopt innovative technologies for their omnichannel strategy and incorporate anthropomorphic design cues.
With the platform owner entering complementors’ product space, the cooperative nature of the platform ecosystem shifts into coopetition, a mixed strategy of cooperation and competition. Since a disproportionately large number of complementors are small in size and suffer from insufficient resources, their platform owner’s unfair competition can pose existential threats to them. Although the literature acknowledges the significance of the research domain, there still exist the challenges of inadequate conceptualization, inefficient measures, and insufficient studies of coopetition. This study attempts to fill the research gap by elucidating the relationship among coopetition balance, capability, and intensity from the complementors’ perspective and by operationalizing the coopetition via latent congruence modeling. The survey data collected from 365 complementors at Amazon suggest that coopetition balance and coopetition capability impact on relationship performance, and that coopetition capability and coopetition intensity moderate the relationship between coopetition balance and relationship performance.
As a development trend of cross-border integration of industries in the digital economy, service-oriented manufacturing has achieved certain research results in the construction of wide value chain. However, in the transition development stage, the collaborative efficiency problem of service-oriented manufacturing value co-creation is increasingly prominent due to the unbalanced development of digitalization level of heterogeneous subjects. Therefore, based on the perspective of various subjects in the value chain of service-oriented manufacturing, this paper analyzes the synergistic mechanism of value co-creation of core manufacturing, service enterprises and customers under the influence of digitalization level difference. Then the paper constructs a three-party digital cooperative evolution game model and uses Netlogo software to simulation analyse. The results reveal the collaborative path of service-oriented manufacturing digital transformation. According to the comparison of the paths of actual enterprises, it is found that the paths has universality, which provide strategic reference for service-oriented manufacturing digital transformation.
Crafting the right keywords and crafting their ad creatives is an arduous task that requires the collaboration of online marketers, creative directors, data scientists, and possibly linguists. Many parts of this craft are still manual and therefore not scalable especially for large e-commerce companies that have big inventories and big search campaigns. Furthermore, the craft is inherently experimental, which means that the marketing team has to experiment with different marketing messages from subtle to strong, with different keywords from broadly relevant (to the product) to exactly/specifically relevant, with different landing pages from informative to transactional, and many other test variants. The failure to experiment quickly for finding what works results in users being dissatisfied and marketing budget being wasted. For rapid experimentation, we set out to generate ad creatives automatically. The process of generating an ad creative from a given landing page is considered as a text summarization problem and we adopted the abstractive text summarization approach. We reported the results of our empirical evaluation on generative adversarial networks and reinforcement learning methods.
We proposed a research model that examined the differences between the contributions of large, third-party e-commerce platforms and self-operated e-commerce platforms to businesses’ resilience to the COVID-19 shock. The difference-in-differences approach was employed to analyze a substantial sample of Chinese retailers. The study found that (1) under the baseline condition, the large, third-party e-commerce platforms built significant resilience for the brick-and-mortar businesses, (2) resource constraints induced by factor immobility weakened the contribution of large, third-party e-commerce platforms to the businesses’ resilience in regions of severe shock, and (3) the physical retailers’ self-operated EC platforms built resilience in regions of severe shock.