The Impact of Online and Offline Information Sources on Automobile Choice Behavior

The Internet has significantly impacted the information search behavior of consumers. Many consumers regularly consult Internet sources for information on product categories, brands, manufacturers, and retailers, particularly when making a purchase decision about major durable goods. Automobiles are one example of such goods. The Internet has become a major source for information on automobile brands, attributes, and dealers. While much research has been done on the impact of the Internet on automobile information search behavior and search costs, there is limited work on the relationship between Internet use and the ultimate automobile choice. This type of relationship may have interesting managerial implications for both manufacturers of automobiles and firms that provide information on automobiles. This paper attempts to address this gap. It examines whether Internet use is associated with different choice patterns for automobiles. Using discrete choice analysis on automobile choice data, we explore whether there will be differences in the salience of specific information types for online versus offline consumers. We find that Internet users rely more on ratings while non-Internet users rely more on recommendations when making automobile choices. Our findings have several useful managerial implications for information provision, both online and offline.     

Comparing sustainable consumption patterns across product sectors

In this paper, we present findings from two qualitative studies where we explored sustainable consumption practices through examining consumers' information search and decision-making processes for recent purchases of five categories of goods/services: fast moving consumer goods (such as foodstuffs and household products), white goods (such as fridges and washing machines), small electrical products (such as TVs and computers), green energy tariffs (such as electricity from renewable sources) and tourism (such as flights). This research has provided us with a set of rich data which explores the nature and extent of sustainable consumption practices across different product sectors. A comparative analysis has allowed us to draw out patterns of consumer behaviour for different product and service types. Our findings suggest that even the same green consumer will not use the same information sources or decision-making criteria, consider the same options or focus on the same industry actors, for products in different sectors. However, we have identified some degree of consistency in purchases within sectors. We present these sector-specific patterns of consumer behaviour and highlight differences in the criteria utilized and the research norms in each sector.     

The Recipe for the Perfect Review?

Online product reviews, originally intended to reduce consumers’ pre-purchase search and evaluation costs, have become so numerous that they are now themselves a source for information overload. To help consumers find high-quality reviews faster, review rankings based on consumers’ evaluations of their helpfulness were introduced. But many reviews are never evaluated and never ranked. Moreover, current helpfulness-based systems provide little or no advice to reviewers on how to write more helpful reviews. Average review quality and consumer search costs could be much improved if these issues were solved. This requires identifying the determinants of review helpfulness, which we carry out based on an adaption of Wang and Strong’s well-known data quality framework. Our empirical analysis shows that review helpfulness is influenced not only by single-review features but also by contextual factors expressing review value relative to all available reviews. Reviews for experiential goods differ systematically from reviews for utilitarian goods. Our findings, based on 27,104 reviews from Amazon.com across six product categories, form the basis for estimating preliminary helpfulness scores for unrated reviews and for developing interactive, personalized review writing support tools.     

How do firms make money selling digital goods online?

We review research on revenue models used by online firms who offer digital goods. Such goods are non-rival, have near zero marginal cost of production and distribution, low marginal cost of consumer search, and low transaction costs. Additionally, firms can easily observe and measure consumer behavior. We start by asking what consumers can offer in exchange for digital goods. We suggest that consumers can offer their money, personal information, or time. Firms, in turn, can generate revenue by selling digital content, brokering consumer information, or showing advertising. We discuss the firm’s trade-off in choosing between the different revenue streams, such as offering paid content or free content while relying on advertising revenues. We then turn to specific challenges firms face when choosing a revenue model based on either content, information, or advertising. Additionally, we discuss nascent revenue models that combine different revenue streams such as crowdfunding (content and information) or blogs (information and advertising). We conclude with a discussion of opportunities for future research including implications for firms’ revenue models from the increasing importance of the mobile Internet.     

Consumer willingness to pay across retail channels

Published research examining differences in consumer willingness to pay for products at omnichannel and pure-play online retailers is based on non-durable, search products and is equivocal; prices at pure-play online retailers are not always lower. This research examines differences in consumer willingness to pay for online purchases of functional and expressive products that differ in the length of product life, i.e. durable and nondurables at omnichannel versus pure play online retailers. Empirical analysis of consumer-centric, multi-site clickstream data of prices paid show that consumers are willing to pay higher prices for home goods products at omnichannel retailers compared to pure-play online retailers but differ for functional and expressive products. Implications are drawn to assist both omnichannel and PPO retailers in their pricing strategies.     

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.     

Multichannel customer purchase behavior and long tail effects in the fashion goods market

The fast-paced growth of e-commerce is impacting the type and variety of products consumers purchase across channels. A commonly held theory, known as long tail theory, posits that online sales are less concentrated at the top of the sales distribution than offline sales, and that more variety is bought online, making the tails of the overall sales distribution denser with the growth of e-commerce. Most of the literature testing the long tail theory has focused on examining entertainment goods markets that do not require much physical examination, and has predominantly found results consistent with the theory. However, the magnitude and antecedents of the observed long tail effects might be different for product categories containing products that require more physical examination before purchase, such as fashion goods. In this study, using detailed individual and transaction level panel data from two multichannel fashion goods retail brands, we show that while the shift to the online channel results in a decrease in the concentration of overall sales for both brands, this change mostly results from consumers buying different products online rather than consumers buying a greater variety online compared to offline. We show that the flattening of the overall sales distribution with the growth of e-commerce in our data is driven by consumers sorting their purchases into channels based on product characteristics. In contrast to the recommendations from the previous long tail literature, our results show that fashion apparel retailers do not need to offer broader assortments online compared to offline, but they may find it profitable to carry or emphasize a different product mix online compared to offline. Our results also provide guidance to fashion goods retailers in curating their online and offline assortments and setting inventory management strategies across the channels.     

The role of the Internet on free-riding: An exploratory study of the wallpaper industry

This study discusses the role of the Internet on possible free-riding activities for product categories where retail services are a critical part of the completed consumer purchase transaction. The study investigates free-riding in terms of consumer pre-purchase activities during the information search stage (how they process shopping information) and actual purchase decisions with a literature review. The study's empirical findings indicate that full-service retailers’ beliefs about online consumers’ choice of purchase outlet are predominantly influenced by online retailer prices rather than availability of a variety of products (place) on the Internet. This, in turn, indicates the possibility of strong free-riding opportunities in the sample wallpaper market. The study, in this context, proposes strategies and policies to eliminate many of the destructive effects of the opportunities for free-riding provided by the Internet for long-lasting channel and market effectiveness and efficiency.     

Designing the content of advertising in a differentiated market

In many markets, consumers use attribute information to assess the value they expect from purchasing a product or service. This includes many low involvement experience goods including take-out food, many packaged good categories and restaurants. In these markets, quality differences exist but many differences are horizontal in nature: the consumer is interested in finding a product that meets her unique tastes. Beyond ensuring that consumers know the brand, the category and the price; it seems advertising should provide consumers with attribute information. However, a significant proportion of advertising does not provide it. In fact, within the same category, competitors respond to messages that emphasize detailed attribute information with messages that are devoid of attribute information. These messages are uninformative about product attributes. We explore how competition in a differentiated market is affected by the ability of a firm has to choose uninformative messages. We construct a model to investigate the factors that affect a firm’s decision to use advertising with detailed attribute information or advertising that does not provide it. The model demonstrates that content decisions about advertising are affected by the differences between products, the range of heterogeneity in consumer tastes and the degree to which costs increase as a function of the quantity of information in advertising. Surprisingly, even when the cost to increase the quantity of information in advertising is low, uninformative campaigns can be more profitable than campaigns with attribute information. The analysis also demonstrates that firms can be more likely to provide attribute information when there are less consumers that are attribute-sensitive. Finally, the model shows that uninformative messages can create “artificial differentiation” in some situations.     

When Do Online Consumers Shop in an Offline Store: The Moderating Effects of Product Characteristics

This study serves as an initial attempt to demonstrate empirically how online consumers react to the offline channel extensions (i.e., opening physical stores) of e-tailers. Specifically, we examine how the attributes of offline channels influence consumer intentions to switch to offline channels and how they also lead to incremental demands on online channels. We investigate how these effects vary across highly complex utilitarian and hedonic products. The results of the study indicate that although store openings encourage online consumers to shop there when purchasing highly complex utilitarian products, the addition of offline stores results counterintuitively in incremental patronage of the online channels when consumers shop for highly complex hedonic products. This study validates the guiding role of product characteristics in designing offline channels for e-tailers and suggests that incorporating product type and complexity into the design is likely to contribute to the development of stores tailored to specific consumer segments.     

Shopping and virtual communities for consumers with physical disabilities

The purpose of this exploratory study was to investigate the use of virtual communities for clothing shopping by consumers with physical disabilities, and to explore the role that these communities play in their clothing shopping experiences. An international virtual community created for consumers with disabilities was identified using keywords through search engine directories. The content of discussion forums was identified and analysed with the assistance of the qualitative research software tool, QSR NVivo 9.0. The content was coded to allow categories and themes to emerge. Thirty‐one categories, which were dispersed between three major themes (self, product, market), emerged from data analysis. In the discussions, various communication channels and social media sites were mentioned, and users enhanced their discussion topics with a photograph of a product or an Internet link, providing further assistance to peers. Consumers also shared personal information, such as life experiences. The results of this study may provide significant implications to retailers in their efforts to target these consumers, in various areas including product development, branding, marketing, customer relationship and managerial strategies. There may also be relevance for development of other consumer goods and sales to other target markets. Using online discussion forums to probe for product development and brand ideas has not been widely addressed, nor has the information search process for people with disabilities. The use of QSR NVivo software is new for a study such as this and should be explored in greater depth for future research with this type of data.     

Product classifications,consumer characteristics,and patronage preference for online auction

An important unanswered question in the area of Internet auctions is what dynamics influences consumer preferences for shopping on these online auctions. Two key aspects of studying what sells on the Internet auctions are (a) the characteristics of products to be purchased and (b) consumer characteristics. This research focuses on the relationship between product purchase preference, prior experience, and socioeconomic characteristics for the online auction participants. It uses product classification theory rooted in information economics and marketing. The study results are based on a US national sample of online auction participants. The findings suggest that product class affects online auction patronage, yet socioeconomic characteristics may not be as influential as once perceived. The online merchants can benefit from the inquiry to improve auctions for search, experience, and credence goods. The five key contributions of the findings add value to the theory and practice of consumer behavior.     

The role of brand exposure and experience on brand recall—Product durables vis-à-vis FMCG

The purpose of this study was to examine the role of brand exposure and experience on brand recall, mediated by three affectional drivers: brand trust, brand image and self-image congruence. The study distinguishes between an individual consumer׳s brand exposure and experience, and how these impact brand recall. Using original data collected from a survey of 219 consumers, brand recall models in two product categories (Fast Moving Consumer Goods FMCG and durable goods) were tested. Structural equation modelling (SEM) was applied, resulting in models for the two types of goods, establishing notable differences between durables and FMCGs. Experience influences brand recall in durable goods while brand recall is established via exposure for fast-moving consumer goods. This suggests that consumers are better able to recall durable goods brands if they have personally used them, while consumers of FMCG are more heavily influenced by advertising. Our study also established that brand trust has a significant role in mediating both of these relationships. Ultimately, the study establishes key differences between the brand recall of product categories, as well as the mediating role of brand trust, providing a framework for future brand recall research.     

The evolving role of hit and niche products in brick-and-mortar retail category assortment planning: A large-scale empirical investigation of U.S. consumer packaged goods

Long tail theory, the notion that the future of retailing could involve shifting product assortments to offer more product variety to precisely serve the unique needs of individual customers, has largely been proven true in the context of online distribution. However, it has been implicitly assumed that this theory does not apply to brick-and-mortar selling situations due to higher supply side inventory costs and higher demand side consumer search costs. Academics and practitioners alike have thus advocated for the use of Pareto rules to make category assortment planning decisions about product inventory breadth and depth in the brick-and-mortar channel. This research directly challenges this received wisdom by noting that the widespread prevalence and use of mobile technology is causing information flows to speed up for consumers even while performing traditional shopping tasks. Such information flows should theoretically favor the strategic importance of niche products in the long tail of the sales distribution. A large-scale empirical analysis of consumer packaged goods brick-and-mortar retailing data indicates that this alternative prediction is indeed true. This research contributes to a larger body of research as well that has documented how major environmental forces are shifting the nature of retail strategy, particularly in the brick-and-mortar channel.     

An empirical analysis of online shopping adoption in Beijing,China

The Internet is a global communication medium that is increasingly being used worldwide as an innovative tool for marketing goods and services. At the end of 2010, Internet users in China reached 420 million. However, online shopping in China is not widely practiced and organisations investing in B2C online shopping need to understand the factors that affect Chinese consumers′ online buying behaviour. This research develops a theoretical research model as a framework to identify the key decision factors influencing Chinese consumers′ to shop, or not to shop online. A self-administered questionnaire was used to gather information from 435 respondents in Beijing, China. The empirical analysis identifies and ranks seven important decision factors: perceived risk, consumer resources, service quality, subjective norms, product variety, convenience, and website factors. All of these decision factors impact on Chinese consumers′ adoption of online shopping. Moreover, managerial implications and recommendations are also presented.     

Buying Groceries in Brick and Click Stores: Category Allocation Decisions and the Moderating Effect of Online Buying Experience

The large majority of online grocery shoppers are multichannel shoppers who keep visiting offline grocery stores to combine convenience advantages of online shopping with self-service advantages of offline stores. An important retail management question, therefore, is how these consumers divide grocery purchases across the retailer's online and offline channel. We provide a comprehensive analysis of the impact of category characteristics on the allocation pattern of multichannel grocery shoppers and find that category allocation decisions are affected not only by marketing mix differences between the online and offline channel, but also by intrinsic category characteristics like perceived purchase risk and shopping convenience. In addition, we examine the effect of online buying experience. In line with expectations, we find that it can affect allocation patterns in different ways: (i) it attenuates the perceived risk of buying sensory categories online, thereby reducing differences in online category share, (ii) it reinforces marketing mix (assortment) effects, thereby making online category share differences more pronounced, and (iii) it has no effect for factors such as promotions that are easy to evaluate without experience, thereby leaving the online category share stable. In addition to different experience effects across allocation factors, we also observe variations in experience effects across consumer segments.     

Why is Price Dispersion Higher Online than Offline? The Impact of Retailer Type and Shopping Risk on Price Dispersion

When physically similar products, of similar quality, are offered by retailers both online and offline, we often observe that the dispersion in prices of these products online is greater than the price dispersion offline. This observation runs counter to early theories that suggested price dispersion online would be smaller than that offline due to the ease of search and information availability online. This paper investigates and provides an explanation for this puzzling phenomenon by examining the impact of two important drivers of price dispersion: retailer type and consumers’ shopping risk. Retailer type refers to whether a retailer is a pure offline, pure online, or dual channel retailer. Shopping risk is defined as the product of consumers’ perceived risk of shopping and the transaction uncertainty related to shopping at different types of retailers.A game-theoretic approach is adopted to model consumers’ price search and product purchase, as well as price competition within and across retailer types in online and offline markets. Equilibrium pricing strategies are derived for different retailer types competing for different consumer segments with different levels of perceived shopping risk. The impact of retailer type and shopping risk on online versus offline price dispersion are quantified, and conditions when price dispersion is greater online than offline are identified.Results indicate that price dispersion is greater online when the number of pure online retailers is sufficiently large and is increasing in the number of pure online retailers. In addition, a reduction in online shopping risk may actually increase online price dispersion. Results further suggest that even without any online sales, dual channel retailers should maintain their online presence for the purpose of information dissemination, which justifies the importance for pure offline retailer to incorporate webrooming strategies, where consumers can search for prices online but purchase offline.     

How to serve online consumers in rural markets: Evidence-based recommendations

Online shopping is growing significantly among rural consumers interested in overcoming product assortment limitations in their local markets. Retailers are taking notice, and some are looking for first-mover-advantage opportunities in rural areas where they see greater potential for growth when compared to saturated urban markets. However, expanding to these areas presents significant and unresolved challenges related to last mile fulfillment caused by low levels of population density as well as other socioeconomic and demographic characteristics. In this article, we present an integrated set of recommendations for online retailers to differentiate strategies to serve rural markets efficiently. To that end, we partnered with an online grocery retailer to examine differences between rural and urban consumers across online shopping preferences that significantly impact fulfillment costs. We draw on those differences and existing knowledge on last mile fulfillment operations to propose a tailored consumer value proposition and operational model for last mile fulfillment in rural markets.     

Multiple-Category Decision-Making: Review and Synthesis

In many purchase environments, consumers use information from a number of product categories prior to making a decision. These purchase situations create dependencies in choice outcomes across categories. As such, these decision problems cannot be easily modeled using the single-category, single-choice paradigm commonly used by researchers in marketing. We outline a conceptual framework for categorization, and then discuss three types of cross-category dependence: cross-category consideration cross-category learning, and product bundling. We argue that the key to modeling choice dependence across categories is knowledge of the goals driving consumer behavior.