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This study aims to determine the antecedents and consequences of information source choice to support a purchase decision for services high in experience attributes.

The authors conduct two studies to test their propositions. Study 1 is a single-category application using data from a national survey of 974 consumers who recently made a hotel-stay purchase/reservation. Correspondence analysis was used to identify search patterns, and regression analysis was used to identify their antecedents and influence on search outcomes. Study 2 is a cross-category study using data from a survey of 422 MTurk respondents reporting on search processes across six different services contexts, including hotel reservations. In this study, the authors seek generalization of their results to other services categories.

The authors identify four dimensions that characterize what information sources consumers, on average, use together when purchasing services. It is found that loyalty program membership and consistency in service delivery across a brand’s outlets for the brands in a consumer’s evoked set are important determinants of search patterns. Search patterns partially mediate the impact of consumer characteristics, choice context and choice set characteristics on search effort and, ultimately, on price paid.

An understanding of the factors that are associated with consumers’ choices of information sources and whether these choices are systematically related to search outcomes has implications for market segmentation and for marketers’ initiatives with respect to what information content to emphasize across sources.

The contribution is an understanding of the antecedents and consequences of consumer search patterns – and what information sources consumers tend to use together, considering the diversity of both internet and non-internet sources. There are limited insights in the services literature regarding how the internet impacts information search processes.

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.

To examine shopping orientation, information search, and demographics of multichannel customers in comparison to traditional single channel customers.

A questionnaire was used to assess research variables and mailed out to 10,000 individuals in South Korea who were randomly selected from a purchased national database. A total of 2,926 usable questionnaires were returned for a 29 percent response rate.

Shopping orientation, information search, and demographics differentiated shopper groups: single‐channel offline users, single‐channel online users, multichannel offline users, and multichannel online users.

A lack of theoretical approaches, a direct self‐assessment for store choice behavior, and duplicated measures for independent and dependent variables perhaps limit its usefulness.

Provides guidance to global retailers who plan to pioneer new markets with multichannel retailing strategies. Shopping orientations, perceived usefulness of information sources, and demographics can be effectively used to identify target markets in Korea.

This study first explored Korean consumer profiles in the context of multi‐shopping channels and added valuable empirical findings to the current limited literature in multichannel retailing in the international market and to help global retailers identify consumer segments based on channel choice behavior.

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 history of the public library is long and rich, and continues to reflect this institution's initial mission: to respond to the needs of an evolving democratic society. From its early days as a subscription service for the middle-class, through its evolution to become an educational site for the lower-classes and new immigrants, the public library has served as a touch-stone for urban industrial society in North America (Lerner, 1998, p. 138; Shera, 1974). Over the past century, public libraries have evolved to respond to the growing needs of the communities they serve and continue to do so with recent advances in technologies (such as DVDs, electronic books, the Internet, etc.), and with a more global outlook on the ways that people seek and share information. Indeed, the public library's constituents today are exceedingly diverse, including children and adults from a broad range of socio-economic, cultural, and educational backgrounds, all of whom seek information for a variety of personal and work-related purposes. The fact that public libraries have been fulfilling patrons' information needs for well over a century is a testament to their enduring success and versatility as information providers, and also points to the overall effectiveness of public librarians as intermediaries in the provision process.

The purpose of this paper is to explore the factors determining which communication mediums influence a given consumer deciding to purchase a specific product.

Using a consumer survey and a multivariate probit (MVP) model, the authors explore consumer information searches related to purchases in nine categories: milk, instant noodles, shampoo, mobile phones, televisions, cars, mobile communication services, credit card services, and life insurance.

The media channels that motivate a given consumer to make a given purchase vary depending on both socio-demographic variables and product categories.

As consumers can now obtain product information through different and multiple media channels according to their personal characteristics and the category of the product they seek to purchase, these findings will help companies develop media planning strategies that will effectively target specific market segments.

Unlike previous studies, the authors consider which media channels actually affect a consumer’s product purchase decisions, and the authors do so across product categories and media types to provide practical implications for media planning. Furthermore, this is the first application of the MVP model in this context.

Starting from intention–behaviour models and building upon the growing evidence that aggregated internet search query data represent a good proxy of human interests and intentions. The purpose of this study is to demonstrate that the internet search traffic information related to the selected key terms associated with establishing new businesses, reflects well the dynamics of entrepreneurial activity in a country and can be used for predicting entrepreneurial activity at the national level.

Theoretical framework is based on intention–behaviour models and supported by the knowledge spillover theory of entrepreneurship. Monthly data on new business registration from 2018 to 2021 is derived from the open database of the Russian Federal Tax Service. Terms of internet search interest are identified through interviews with the recent founders of new businesses, whereas the internet search query statistics on the identified terms are obtained from Google Trends and Yandex Wordstat.

The results suggest that aggregated data about web searches related to opening a new business in a country is positively correlated with the dynamics of entrepreneurial activity in the country and, as such, may be useful for predicting the level of that activity.

The results may serve as a starting point for a new approach to measure, monitor and predict entrepreneurial activities in a country and can help in better addressing policymaking issues related to entrepreneurship.

To the best of the authors’ knowledge, this study is original in its approach and results. Building on intention–behaviour models, this study outlines, to the best of the authors’ knowledge, the first usage of big data for analysing the intention–behaviour relationship in entrepreneurship. This study also contributes to the ongoing debate about the value of big data for entrepreneurship research by proposing and demonstrating the credibility of internet search query data as a novel source of quality data in analysing and predicting a country’s entrepreneurial activity.

To investigate and identify the patterns of interaction between searchers and search engine during web searching.

The authors examined 2,465,145 interactions from 534,507 users of Dogpile.com submitted on May 6, 2005, and compared query reformulation patterns. They investigated the type of query modifications and query modification transitions within sessions.

The paper identifies three strong query reformulation transition patterns: between specialization and generalization; between video and audio, and between content change and system assistance. In addition, the findings show that web and images content were the most popular media collections.

This research sheds light on the more complex aspects of web searching involving query modifications.

Internet data hold many promises for demographic research, but come with severe drawbacks due to several types of bias. The purpose of this paper is to review the literature that uses internet data for demographic studies and presents a general framework for addressing the problem of selection bias in non-representative samples.

The authors propose two main approaches to reduce bias. When ground truth data are available, the authors suggest a method that relies on calibration of the online data against reliable official statistics. When no ground truth data are available, the authors propose a difference in differences approach to evaluate relative trends.

The authors offer a generalization of existing techniques. Although there is not a definite answer to the question of whether statistical inference can be made from non-representative samples, the authors show that, when certain assumptions are met, the authors can extract signal from noisy and biased data.

The methods are sensitive to a number of assumptions. These include some regularities in the way the bias changes across different locations, different demographic groups and between time steps. The assumptions that we discuss might not always hold. In particular, the scenario where bias varies in an unpredictable manner and, at the same time, there is no “ground truth” available to continuously calibrate the model, remains challenging and beyond the scope of this paper.

The paper combines a critical review of existing substantive and methodological literature with a generalization of prior techniques. It intends to provide a fresh perspective on the issue and to stimulate the methodological discussion among social scientists.