Search results

This research reviews numerous studies of the relationship between consumer knowledge and external search in conventional marketing channels to investigate differences among these studies that have produced conflicting results. The findings provide a benchmark for future researchers and practitioners seeking to gain insight into consumer information search processes unfolding in the new environment of online, mobile, and social networking channels.

A meta-analysis of an extensive array of empirical studies of the relationship between consumer knowledge and external information search was conducted. Regression analysis was used to test whether certain characteristics in the studies can explain variability in the effect sizes in which effect sizes are entered as dependent variables and moderators as independent variables.

Objective and subjective knowledge tend to increase search, while direct experience tends to reduce search. Consumers with higher objective knowledge search more when pursuing credence products. However, they search relatively less when pursuing search products. Consumers with higher subjective knowledge are much more likely to search in the context of experience products, but as is the case for objective knowledge having little effect on search for experience products, subjective knowledge has no significant effect on information seeking for search products. In addition, objective knowledge facilitates more information search in a complex decision-making context while higher subjective knowledge fosters more external information search in a simple decision-marketing context. Finally, the findings indicate that the knowledge search relationship reflects strong linkage in the pre-Internet era.

Relatively little is known about how the relationship between knowledge and information search varies across different types of products in simple or complex decision-making contexts. This study begins to fill this gap by providing insight into the relative importance of objective knowledge, subjective knowledge, and direct experience in influencing consumer information search activities for search, experience, and credence products in simple or complex decision-making contexts.

This paper aims to demonstrate how consumer clickstream data from a leading hotel search engine can be used to validate two hidden information processing stages – first eliminate alternatives, then choose – proposed by the revered information processing theory of consumer choice.

This study models the two hidden information processing stages as hidden states in a hidden Markov model, estimated on consumer search behavior, product attributes and diversity of alternatives in the consideration set.

First, the stage of information processing can be statistically characterized in terms of consumer search covariates, including trip characteristics, use of search tools and the diversity of the consideration set, operationalized in terms of: number of brands, dispersion of price and dispersion of quality. Second, users are more sensitive to price and quality in the first rather than the second stage, which is closer to purchase.

The results suggest practical implications for how search engine managers can target consumers with appropriate marketing-mix actions, based on which information processing stage consumers might be in.

Most previous studies on validating the information processing theory of consumer choice have used laboratory experiments, subjects and information display boards comprising hypothetical product alternatives and attributes. Only a few studies use observational data. In contrast, this study uniquely uses point-of-purchase clickstream data on actual visitors at a leading hotel search engine and tests the theory based on real products, attributes and diversity of the consideration set.

The purpose of this research is to propose and validate a theoretical framework explaining web-rooming and showrooming as a multi-stage decision-making process. The authors have used consumer purchase decision-making theories to propose a model that identifies showrooming and webrooming as a combination of two decisions, channel choice during information search and channel choice during actual purchase. Further, the authors explored how various antecedents of showrooming and webrooming have differential effects on various stages of a purchase decision-making process and how product type moderates the relationships.

The authors have conducted empirical research, whereby 243 responses were obtained from a cross-sectional survey. The authors have used structural equation modeling and multiple regression analysis to validate our theoretical model.

Webrooming or showrooming is a multi-stage decision-making process for the consumers. First, consumers decide whether to search online or offline and then whether to buy online and offline. Different individual, purchase context-related and channel related factors impact these decisions. Product type governs which variables will be more important than others.

The research looks to enhance the understanding of the consumer's decision-making process during showrooming and webrooming while also helping retailers design and implement appropriate strategies that could affect consumers during information search and actual purchase.

As the potential of the Internet is gradually realized and its importance progressively recognized, waves of companies are now trying to utilize this new means of commerce by conducting business online and offering their products for cyber‐exchanges. Suppliers in the construction industry, coming to understand the advantages of e‐commerce, have also begun putting their product catalogues online. However, before suppliers can truly benefit from e‐commerce, a comprehensive and user‐friendly electronic product catalogue must be developed so that buyers can easily navigate through e‐markets to find what they need. This paper attempts to review problems of existing electronic product catalogues on construction material websites and presents an electronic product catalogue designed to facilitate easy searching, comparing and evaluating of construction materials.

This study examines consumers' channel attitudes and choices leading to webrooming and showrooming, and how product attributes (informational vs experiential and perceived risk) moderate the effects of channel attitudes.

A research framework is built upon the heterogeneity of channel attitudes, the lack of intrachannel lock-in and interchannel synergy. A questionnaire-based survey yields 868 multi-channel consumer responses in China. Simultaneous equation modeling and STATA 12.0 are used to test the hypotheses.

Consumers webroom when buying high-risk informational products (e.g. personal computers or mobile phones). They webroom as well as showroom for high-risk experiential products (e.g. clothing or cosmetics). Moreover, a single channel is preferred to webrooming or showrooming for purchasing low-risk informational (e.g. books or stationery) and low-risk experiential (e.g. snacks or toys) products. The results also show that webrooming is more frequently used than showrooming by consumers.

This study extends current understanding on multi-channel and omnichannel shopping behavior and highlights the role of product attributes in customer journey mapping.

This study offers retailers and other downstream firms a fresh perspective on multi-channel customer experience management and channel design.

This study offers a clear explanation on the commonalities and differences between webrooming and showrooming.

The purpose of this research is to help better understand the impact of online user reviews on sales of search goods.

This research is based on digital camera sales data collected from amazon.com and two studies are included in this research. The first study is based on a static model and sample data from one time stamp. The second study is based on two sample data collected from two different time stamps, and a dynamic model is proposed.

The results from the first study reveal that the average online customer review, the number of online reviews, the price and the camera's physical properties such as the number of pixels and the optimal zoom number (but not LCD screen size) have significant influence on digital camera sales. The results from the second study show that the sales from the previous period are an important indicator for future sales. In addition, change in price, change in average online review rating and change in the total number of online reviews are all significantly associated with future sales.

The research reveals that there is a significant relationship between the online user review and sales of search goods, and the influence of online user reviews on search goods sales is different from that on experience goods. It also recognizes that the product specifications influence the sales of search goods. In addition, the research on search goods shows that price at the specific time and price changes are significant factors affecting sales.

The research indicates that retailers should provide channels for, and encourage, customer online reviews for search goods to improve sales. It is also beneficial for online retailers to provide detailed product attributes to help their customers make the purchase decision. Carefully designed and executed price promotions could also be effective ways to improve sales of searchable goods.

This study is one of the first attempts to investigate the impact of online user reviews on sales of search goods.

Searching the tender notices that publish every day in open tendering websites is a common way for finding business opportunity in public procurement. The heterogeneity of tender notices from various tendering marketplaces is a challenge for exploiting semantic technologies in the tender search.

Most of the semantic matching approaches require the data to be structured and integrated according to a data model. But the integration process can be expensive and time-consuming especially for multi-source data integration.

In this paper, a product search mechanism that had been developed in an e-procurement platform for matching product e-catalogues is applied to the tender search problem. The search performance has been compared using two procurement vocabularies on searching tender notices from two major tender resources.

The test results show that the matching mechanism is able to find tender notices from heterogeneous resources and different classification systems without transforming the tenders to a uniform data model.

We review the implications of the mobile technology for different stages of the consumer path to purchase including awareness, search, evaluation, store visit, and product choice. Real-time and location-specific access to information and products are identified as distinguishing characteristics of mobile devices. While the literature on digital marketing is well developed, knowledge of the effects on the consumer path to purchase in the presence of dynamic and location-specific information is still scarce. Path to purchase models need to recognize the central and powerful role of user-generated content. Better management of marketing resources would require models that connect investments in mobile marketing to sales, and also model the synergies among different digital and offline media. We conclude with a framework that connects mobile media impressions to product choice, in the presence of other marketing media, and consumer and firm feedback loops.

The purpose of this paper is to analyze the relationship between new product diffusion and consumer internet search patterns using big data and to investigate whether such data can be used in forecasting new product diffusion.

This research proposes a new product diffusion model based on the Bass diffusion model by incorporating consumer internet search behavior. Actual data from search engine queries and new vehicle sales for each vehicle class and region are used to estimate the proposed model. Statistical analyses are used to interpret the estimated results, and the prediction performance of the proposed method is compared with other methods to validate the usefulness of data for internet search engine queries in forecasting new product diffusion.

The estimated coefficients of the proposed model provide a clear interpretation of the relationship between new product diffusion and internet search volume. In 83.62 percent of 218 cases, analyzing the internet search pattern data are significant to explain new product diffusion and that internet search volume helps to predict new product diffusion. Therefore, marketing that seeks to increase internet search volume could positively affect vehicle sales. In addition, the demand forecasting performance of the proposed diffusion model is superior to those of other models for both long-term and short-term predictions.

As search queries have only been available since 2004, comparisons with data from earlier years are not possible. The proposed model can be extended using other big data from additional sources.

This research directly demonstrates the relationship between new product diffusion and consumer internet search pattern and investigates whether internet search queries can be used to forecast new product diffusion by product type and region. Based on the estimated results, increasing internet search volume could positively affect vehicle sales across product types and regions. Because the proposed model had the best prediction power compared with the other considered models for all cases with large margins, it can be successfully utilized in forecasting demand for new products.