Research needs for assessing online value creation in complex consumer purchase process behavior

Consumers face considerable frustration when purchasing structurally and/or semantically complex high-involvement products online. Reliance on computer-mediated communications for their information needs may result in functional and emotional frustration from information overload and lack of personal trust. This paper proposes a responsive real-time information system as a proxy for a perceptive sales representative who assesses customer needs based on information exchanges and then offers appropriate responses. By tracking and analyzing a consumer's online activity, vendors can offer information relevant to the consumer's real-time needs, facilitating their purchase process. In essence, this is a real-time value co-creation process based on the consumer offering cues to vendors through their key strokes and mouse click activity. This allows for differentiated information offerings for inexperienced and more experienced consumers, creating value by dynamic information serving. Where appropriate value is created, consumers will experience less frustration and continue online, rather than possibly moving offline or to alternative vendors. In examining the bases of consumer information needs in complex purchases, this paper identifies the data required to enable a responsive dialog between vendors and consumers.     

Cross-selling across stores or within a store? Impacts of cross-buying behavior in online shopping malls

Abstract

This study investigates the impacts of cross-buying behavior in an online shopping mall where customers can purchase from additional product categories from various independent stores. We focus on the extent to which store loyalty and relationship duration moderate the cross-buying effects on three behavioral traits: customers’ purchase rate, lifetime duration, and spending. The results reveal that customers who engage in cross-buying more intensely purchase more frequently, have longer expected lifetime duration, and spend higher amounts in each transaction. The impacts on purchase frequency and customer retention are even greater for customers who exhibit higher behavioral loyalty toward some stores. However, store loyalty is found to weaken the association between cross-buying and average spending. Further, relationship duration appears to weaken the effects of cross-buying on purchase rate and spending. These results provide new insights into the impacts of cross-buying on customer value as well as managerial implications for shopping mall owners.     

Comparing consumer-produced product reviews across multiple websites with sentiment classification

ABSTRACT

Online consumer reviews have been extensively studied. However, existing literature analyzing online consumer review data mostly relies on a single data source, resulting in potentially biased analytics conclusions. Many websites encourage consumers to post reviews of their purchased products, so that new consumers can evaluate these reviews for the same product across different websites to help them make purchasing decisions. Confusions often arise in this process, because there often exist substantial discrepancies in customer reviews across different retailers on the same product. Clarifying such confusions can help consumers reduce concerns to make up their mind for their purchases, therefore benefiting both consumers and retailers. Through text analytics and sentiment analysis, we comparatively examine the underlying patterns of online consumer reviews of three large retailers including Sears, Home Depot, and Best Buy for a same product. Afterward, we combine online consumer reviews from these large retailers and conduct an overall text analytics and sentiment analysis. The overall results are further compared with the results from individual retailers. The findings show that the sentiment of the online consumer reviews could vary substantially so relying on a single data source to make purchase decision is not a wise idea. Based on the results, we further devise a framework to comparatively examine and integrate multiple data sources for social media analytics of online consumer reviews. This study offers important managerial implications and identifies several new research directions for social media analytics.     

The Impact of a Third-Party Assurance Seal in Customer Purchasing Intention

ABSTRACT

Although e-commerce is evolving at an incredible speed, asymmetric information and opportunism have increased online purchasing risks and market inefficiencies. The lack of face-to-face contact and the inability of the customer to touch and feel tangible products necessitate that online business must provide customers with reliable information and convince customers of their trustworthiness. While the research on Third-Party Assurance Seals (TPAS) is not new, the effectiveness of TPAS has not been persuasively addressed by empirical studies. Our study is different from previous efforts in that it is comprehensive because it combines consumer trust, perceived risk, and influences of TPAS in the B2C e-commerce context. The results of the study show that (1) the effects of TPAS on perceived risk are channeled through perceived trustworthiness, thus demonstrating the mediating role of perceived trustworthiness in B2C e-commerce, (2) perceived risk, perceived usefulness of website, and subjective norm appear to be significant predictors of intention to purchase from the website, (3) a third-party assurance seal strongly affects online shoppers' perceived trustworthiness toward online retailers, while the seal appears to have little effect on perceived risk directly; perceived risk is strongly associated with intention to purchase from the website.     

The impact of customer experience on consumer purchase intention in cross-border E-commerce——Taking network structural embeddedness as mediator variable

This study investigates the mediator effect of network structural embeddedness between customer experience and consumer purchase intention in the context of cross-border e-commerce. 321 copies of online questionnaires unfold cross-border online shopping experience. The structural equation model is built to analyze the mediator effect of network structural embeddedness. Network density, Network centrality separately plays a mediating role between website ease of use, website relationship service and consumer purchase intention. In contrast, both of them play a mediating role between customer cost and consumer purchase intention. The research suggests developing the relationship between consumers and e-commerce websites for cross-border e-commerce platforms from a network standpoint, but not only marketing.     

What do consumers want? A methodological framework to identify determinant product attributes from consumers’ online questions

Determinant attributes play an important role in consumers’ purchase decisions. Firms rely on them to differentiate their products. Determinant attributes are typically identified using direct methods or indirect methods. However, the voice of the customer online can also provide key insights regarding attributes that are part of the consumers’ pre-purchase search process. The purpose of this study is to propose a framework to identify determinant attributes from online consumer questions. Our method uses semi-supervised Latent Dirichlet Allocation to identify product attributes initially. This is followed by the application of sequence pattern mining to identify temporal sequences of determinant attributes. Finally, hierarchical time series is used to forecast consumer interest in determinant attributes over time. The results show that our study can be used to identify determinant attributes of competing brands and can also be used to forecast consumer interest in these attributes. Brands can use this framework to have a pulse on what consumers look for before purchase. This will aid their decisions related to promotion as well as product development processes.     

Does online relationship marketing enhance customer retention and cross-buying?

One characteristic of online consumer behavior is the low cost of searching for alternatives. Therefore, customer loyalty is more difficult to achieve in the online context than in the offline one. Although studies have discussed the effectiveness of relationship marketing, nearly all studies examining such constructs have been in the context of ‘offline’ consumer behavior. With regard to the ‘online’ context, the integration of Internet technology with the customer loyalty concept is rarely discussed in the relationship marketing literature. Furthermore, empirical research that integrates the actual purchase behavior of customer retention and cross-buying is relatively sparse. In response to this, the current study develops and empirically tests a model that examines the relations among relationship-bonding tactics, perceived relationship investment, perceived relationship quality, customer loyalty, customer retention, and cross-buying. Using survey data from 766 online customers of a securities corporation, the results demonstrate the effectiveness of relationship marketing as it positively influences online customers' perception, then their loyalty, and ultimately, their actual purchase behavior.     

O2O生鲜电商平台消费者重购意愿影响机制

基于迁徙理论,从驱动与锁定双视角出发,从推力因素、拉力因素和锁定因素三个层面对线上线下融合(O2O)生鲜电商平台重复购买意愿影响因素与内在机理进行实证研究。通过网络调查问卷采集886份消费者样本数据,运用结构方程模型对研究假说进行检验,结果表明,影响O2O生鲜电商平台重复购买意愿具有驱动效应和锁定效应两大机制,其中顾客满意度是驱动效应中的中介变量,顾客惰性是锁定效应中的中介变量。此外,推力因素的便利性、拉力因素的感知价值、锁定因素的转换成本、主观规范与在线涉入度均对消费者重复购买意愿产生正向影响。在后疫情时代,O2O生鲜电商企业应重视以消费者为中心的关系型营销理念,强化体验营销手段,在满足消费者便利性需求与高价值感受的同时,增强消费者对O2O生鲜电商平台的交易依赖,提高消费者黏性。     

Managing customer experiences in online product communities

Many companies have established online product communities or forums as a vehicle to extend product support services to their customers. Customers' interactions in such online forums with peer customers and vendor representatives resolve their product-related queries as well as inform on their product purchase decisions. Despite the significance of such interactions, there has been limited theoretical attention so far on how companies can manage customer experiences in online product communities. Drawing on theories and concepts from diverse areas including computer-mediated communication, consumer psychology, and online communities, this study proposes a four dimensional construct - Online Community Experience (OCE) - to capture customer experiences in such online product communities, and examines its impact on customer attitudes regarding the product, the company, and the quality of service. Data collected from customers in online product forums offered by four companies is used to test the study hypotheses. Implications for research on online consumer behavior and marketing are discussed.     

Collaborating online: the roles of interactivity and personalization

Collaborating with customers is considered a new source of competitive advantage so customer participation and involvement are emerging as key strategic factors. This research studies how interactivity and personalization influence both customers' participation during the online purchase of information services and their intentions to continue participating. It also analyzes whether personalization and interactivity improve customer involvement with the service purchased in online environments. Results verify the importance of interactivity and personalization to foster customer participation, involvement and intentions to continue participating. Moreover, it is found that interactivity moderates the effect of personalization, increasing its influence on service involvement and intentions to participate. This paper demonstrates the convenience of analyzing involvement and participation together in order to understand customer collaboration, as well as the importance of the purchase context from a participation and socialization perspective in the services arena.     

Determining Customer Satisfaction From Mobile Phones: A Neural Network Approach

Abstract

This research examines the effects of extension mode (online to online vs. online to offline), web-brand concept (function oriented vs. experience oriented) and extension similarity (high vs. low) on consumer web-brand extension evaluations, attitude, perceived quality, and purchase intention of the extension brands. The analysis reveals two significant interaction effects: (1) the positive effect of brand extension similarity on consumer evaluations is stronger when the parent brand is function oriented compared to the experience-orientated ones; (2) the positive effect of brand extension similarity on consumer evaluations is more pronounced when the web-brands extend to offline markets rather than to online markets. Implications for marketing theory and practice are discussed.     

Uncertainty representation and risk management for direct segmented marketing

ABSTRACT

Mining for truly responsive customers has become an integral part of customer portfolio management, and, combined with operational tactics to reach these customers, requires an integrated approach to meeting customer needs that often involves the application of concepts from traditionally distinct fields: marketing, statistics, and operations research. This article brings such concepts together to address customer value and revenue maximisation as well as risk minimisation for direct marketing decision-making problems under uncertainty. We focus on customer lift optimisation given the uncertainty associated with lift estimation models, and develop risk management and operational tools for the multiple treatment (recommendation) problem using stochastic and robust optimisation techniques. Results from numerical experiments are presented to illustrate the effect of incorporating uncertainty on the performance of recommendation models.     

Keyword Selection Strategies in Search Engine Optimization: How Relevant is Relevance?

We build an empirical framework using search queries and organic click data which provides model-based guidance to SEO practitioners for keyword selection and web content creation. Specifically, we study how search characteristics (search query popularity, search query competition, search query specificity, and search intent) and website characteristics (content relevance and online authority) interact to affect the expected organic clicks as well as the organic rank a website receives from the search engine result page (SERP). It is often thought that content relevance is a key factor to improve the effectiveness of SEO. We find, however, that content relevance is an important factor in driving organic clicks only when the consumer is farther along in the customer journey and searching for ways to purchase a product. Whereas, when the customer is at the awareness stage and looking for product information, online authority is the key driver of organic clicks.     

Multilevel Information Presentation Strategy and Customer Reaction: An Empirical Investigation in an Online Setting

This study investigates the use of polyinstantiated information in management of customer relationships. Polyinstantiation can be used to present different information to different customers who are segmented according to some criterion, such as prior purchase behavior. An empirical study shows that presentation of multilevel benefits information affects customer satisfaction with the offer and information quality which, in turn, affect the overall Web satisfaction. This effect is independent of and comparable in size to the effect of customer prior attitude toward the Web and the Internet. Although the present study employs a Web-based retail setting, the relationships between this information presentation approach and measures of user/consumer satisfaction need not be limited to retail scenarios or to online interactions. We discuss the applicability of this technique of information control to different types of interactions between organizations and their constituents.     

Electronic Logistics Service Quality (e‐LSQ): Its Impact on the Customer’s Purchase Satisfaction and Retention

The Internet retailing industry continues to grow rapidly. Several Internet retailers are, however, struggling to retain customers due to the high level of competition among incumbents. We propose that customer satisfaction with the order fulfillment process is an important determinant of overall customer satisfaction with the retailer, as well as with the extent of customer retention enjoyed by the retailer. This paper offers a new concept for electronic logistics service quality and investigates the relationship between the quality of online fulfillment and the ensuing retention of customers, using archival data on 260 online retailers. The structural equation model results indicate that satisfaction with the physical distribution quality and cost are positively related with customer’s purchase satisfaction and customer retention. Additionally, the results indicate that while purchase satisfaction is a strong indicator of customer retention, underlying drivers of purchase satisfaction do not have nearly the same impact on customer retention directly as they have on purchase satisfaction.     

How do customers meet their needs in in-store and online fashion shopping? A comparative study based on the jobs-to-be-done theory

Understanding customer needs is key for fashion retailers to stay competitive and innovative. Surprisingly, however, extant literature mainly explores customer needs in terms of a garment and its attributes rather than viewing shopping as a problem-solving process to meet customer needs. Moreover, these studies fail to address how customers meet their needs in-store (ISFR) and online fashion retailing (OFR). To fill this research gap, we empirically investigate customers' personal and social needs and how they can be met through the jobs-to-be-done theory. Findings reveal that, beyond the purchase of a garment, customer needs can be fulfilled through different ways, such as smart technology or a person's high interaction with social others in ISFR and the online shop experience or a social linkage without social interaction in OFR. Additionally, our findings offer potential service innovations for fashion retailing managers.     

Consumers' SKU choices in an online supermarket: a latent class approach

Abstract

This study examines the effects of loyalty and e-marketing mix variables on the choices of online consumers at the stock-keeping-unit (SKU) level. Using a panel dataset from an online supermarket, we estimate a discrete choice model of a frequently purchased product; generate the refined smoothing constants of the loyalty variables for brand, size, and SKU; and adopt the latent class approach to address consumer heterogeneity. The findings suggest that SKU loyalty is a better predictor of consumer choices than brand and size loyalty. Although online consumers are not sensitive to the net prices of SKU alternatives, they are attracted to price promotions. While webpage display has little effect on SKU choices, speedy delivery has a positive impact. The latent class approach significantly improves model fitness and classification accuracy. Analysing consumer choices at the SKU level can help online supermarkets with promotion planning and inventory and distribution management to improve customer satisfaction and profitability.     

Customer segmentation by web content mining

This article introduces a new dimension, Interpurchase Time (T), into the existing RFM (Recency, Frequency, and Monetary) model to form an expanded RFMT model for parsing consumers' online purchase sequences in a long period to implement customer segmentation. The proposed RFMT model can track and discern changes in customer purchasing behaviors during their whole shopping cycle. Firstly, a web content retrieving system was developed to fetch publicly available customer data on a retailer's website, including demographic information (gender, age, location, etc.) and product information (name, price, date, etc.) of each purchase in a period from 2008 to 2019. The RFMT values of a customer were then computed from the retrieved data and subsequently analyzed by the hierarchical clustering to derive seven homogeneous clusters with specific customer profiles. Subsequently, demographic features and product preferences were identified for each cluster with business insights that can help the retailer to improve customer relationships and to implement targeted recommendation strategies.