Impact of Information Technology on Information Search Channel Selection for Consumers

Impact of Information Technology on Information Search Channel Selection for Consumers

Gaurav Khatwani (Indian Institute of Management, Rohtak, India) and Praveen Ranjan Srivastava (Indian Institute of Management, Rohtak, India)
Copyright: © 2018 |Pages: 18
DOI: 10.4018/JOEUC.2018070104

Abstract

As information technology has evolved, digital media has become increasingly fragmented and has started to proliferate multiple information channels. In order to optimize on the various digital channels that are available, organizations are increasingly recognizing the importance of gaining solid insights into consumer behavior and preferences that can be translated into marketing strategies. Specifically, they are keen to identify which information channels they can use to effectively reach and communicate with their target market. In this regard, this paper describes how multi criteria decision making can be used to develop a new method of decision making that will enable an effective and systematic decision process of fuzzy AHP and TOPSIS. Further, these techniques can be used for the developing framework for identifying consumer preferences. This paper provides a demonstration of the underpinning working methodology of the proposed model by examining an real case that is based on the decision process Internet users employ during their online search for information.
Article Preview

1. Introduction

Consumer decision-making approaches can be understood as a series of five progressive stages: need recognition, pre-purchase, evaluation of alternatives, purchase and post-purchase (Grewal & Levy, 2013). The Internet provides an important real-time communication channel that serves to connect marketers with potential customers who are searching online for information pertaining to a particular product or service (Jarvis & McElroy, 2004; Kelley & Jugenheimer, 2008; Saunders, 2004; Soberman, 2005; Winchester & Lees, 2016). A pre-purchase information search model can be used to describe the process by which individuals search for information from two broad categories: a) internal information search/long-term memory and b) external information search (Crotts, 1999). When adopting this approach, individuals typically commence their search by attempting to recall information from their long-term memory (internal search) and will subsequently use an external approach if he/she fails to gather the information needed at the initial stage. External information search channels from an e-commerce users’ perspective can be categorized as follows: a) personal (advice from friends or relatives using social media channels) (A1); b) marketer-dominated (brochure on emails, online advertisements, or media) (A2); c) neutral (discussion forums, information blogs intended for public viewing) (A3); and d) experiential sources (online demo of products through review websites) (A4) (Crotts, 1999; Luo & Bu, 2016).

These channels are defined in the context of the assumption that an Internet user who is completing an online search for information pertaining to a product or service will progress to make a purchase after the information search phase is complete. The four channels outlined above have been evaluated based on the following criteria (Moon, 2004; Stigler, 1961): (1) Quantity of information (C1), (2) design (C2), (3) speed of access and transmission (C3), (4) the extent to which the search structure is user friendly (C4), (5) pace of update (C5), (6) perceived time (C6), (7) cost of search (C7).

These criteria can have a direct impact on the process by which consumers search for pre-purchase information on the Internet before moving ahead to the next stage of evaluating the alternative products/services that are available. Although existing studies have examined the relative importance of each channel individually (Khatwani et al., 2015), existing research has yet to establish how channels work in different tier of cities to influence the pre-purchase information search stage. This study aims to explore user preferences for the amalgamation of Internet information search channels for a variety of different demographic factors. Since technology is changing very fast and people are transitioning between technologies so it is important to identify the impact of channel preferences for different cities mapped (Russell, 2016).

A large amount of literature pertaining to consumer purchase processes argues that the decision making process that consumers employ can vary across demographics. For example, (Larichev & Moshkovich, 1995; Renatus & Geldermann, 2016) argued that males prefer to simplify the purchase process by consulting a limited number of information sources, whereas females prefer to use more extensive sources. To the best of available literature (Grewal & Levy, 2013; Winchester & Lees, 2016), the role that demographic factors can play in influencing how users interact with different information search channels remains relatively less studied. As such, this study aims to examine the moderating roles of demographic parameters, such as different tier of cities, to determine the extent to which they influence a user’s preferred Internet information search channel. Further, such study will help in narrowing gap of technology on consumer channel preferences for the different category of cities. Moreover, this can help in developing highly target advertisements for the location specific services and content marketing.

Complete Article List

Search this Journal:
Reset
Open Access Articles
Volume 32: 4 Issues (2020): 1 Released, 3 Forthcoming
Volume 31: 4 Issues (2019): 3 Released, 1 Forthcoming
Volume 30: 4 Issues (2018)
Volume 29: 4 Issues (2017)
Volume 28: 4 Issues (2016)
Volume 27: 4 Issues (2015)
Volume 26: 4 Issues (2014)
Volume 25: 4 Issues (2013)
Volume 24: 4 Issues (2012)
Volume 23: 4 Issues (2011)
Volume 22: 4 Issues (2010)
Volume 21: 4 Issues (2009)
Volume 20: 4 Issues (2008)
Volume 19: 4 Issues (2007)
Volume 18: 4 Issues (2006)
Volume 17: 4 Issues (2005)
Volume 16: 4 Issues (2004)
Volume 15: 4 Issues (2003)
Volume 14: 4 Issues (2002)
Volume 13: 4 Issues (2001)
Volume 12: 4 Issues (2000)
Volume 11: 4 Issues (1999)
Volume 10: 4 Issues (1998)
Volume 9: 4 Issues (1997)
Volume 8: 4 Issues (1996)
Volume 7: 4 Issues (1995)
Volume 6: 4 Issues (1994)
Volume 5: 4 Issues (1993)
Volume 4: 4 Issues (1992)
Volume 3: 4 Issues (1991)
Volume 2: 4 Issues (1990)
Volume 1: 3 Issues (1989)
View Complete Journal Contents Listing