INTERNET-ENABLED COLLECTIVE INTELLIGENCE AS A PRECURSOR AND PREDICTOR OF CONSUMER BEHAVIOUR.

Author:Carter, Stephen
Position:Report
 
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Introduction

The so-called "millennials" (those born up to 2000) are one the largest purchasing segments after the "baby boomers." Oracle estimated that the spending power of US millennials alone would reach US$ 3.39 trillion by 2018. A segment with such scale has coveted purchasing power. Moreover, this generation (depending on where in the world they were born) grew up in a time of immense and fast-paced technological change, virtually full employment opportunities for women, dual-income households as standard, a wide array of family types seen as normal and a significant respect for ethnic and cultural diversity including a heightened social awareness (Williams and Page, 2011). Moreover, Williams and Page (2011) tellingly identified that millennials have a distrust of brands, but trust of ethical and "authentic" brands and computers in the home and schools. Qader and Omar (2015) found in their study that the millennial generation in Malaysia were potential leaders, consumers, and users with great purchasing power that would shape the country's social, economic and political landscape. Moreover, the same authors found that the majority of millennials in Malaysia were smartphone users and were extensively utilizing their gadgets to stay connected, engaged, and informed, mainly through social media networks, often sharing information (Rong et al., 2012). According to Deloitte LLP (2014), it was not just a Malaysian phenomenon; in Britain and Germany, 56% of all consumers in 2013 used mobile devices to inform themselves before the actual purchase. In addition, PWC (2016) put forward the argument that especially social media, although still trying to find its way to drive online purchasing, has become already one of the greatest influencers on consumers shopping decisions. Further, PWC (2013) showed that 45% of consumers said that reading reviews, comments and feedback in social media influenced their buying decision and thus their overall shopping behaviour, the internet site TripAdvisor.com being a prime example. Steimel, Gentsch, and Dimitrova (2012) argued that fewer people trust traditional advertising but rather increasingly inform and look at opinions of people in the network. Not only that, purchases of goods and services and even the outlets where these were purchased, were also being informed by opinion leaders (Chu and Kim, 2011) through a language of communication (sentiment) of their own (Bronnimann, 2013). This Collective Intelligence (CI) (Gloor, 2006; Kornrumpf and Baumol, 2013) can be a very powerful informant, not only for the type of goods and services bought but where, when and how they are obtained.

Whilst studies have looked at the influence of social media on the purchase of goods and services (e.g. Duffett, 2015; Zhu et al., 2016; Kim and Kim, 2018), there has been no study which combines the four elements of CI (form of communication, language of communication, criteria for choice, opinion leaders and influencers on both prior and post-purchase behaviour) and its predictive effect on the consumer purchase process. There have been studies which have captured "Big Data" (open social media) to help in price prediction of oil prices, for example Ahmed and Shabri (2014), but none that have examined the four elements of CI and their effect as in this study. The ability to do so should enable marketers to more understand the effect of CI and its effect on the consumer purchasing process, so be more precise in their ability to influence it. The reason being that millennials, in particular, are "breaking the traditional purchase decision mode" as well as breaking "the typical socio-demographic to product purchase match" mode, so understanding their preferred form, influences, type and language of communication, coupled with the power of computer generated predictive ability should give marketers more insights into how to influence the millennial purchasing process. A typical purchase cycle is a step like process from "realisation of need" to "search for information" to the "establishment of purchase criteria" to the "actual purchase" and "the post-purchase decision" (dissonance) (Assael, 1995). The more a marketer understands the process, the forms of communication, language of communication and the influences on the process, in a collective group way, the more efficient and effective marketing will be. This exploratory study attempted to discover such collective intelligence in a qualitative way as a test to secure the efficacy of principal components and precursor to a wider, more "machine/human interface" study.

The objectives of this exploratory study were, therefore:

  1. To identify the main forms of open media communications for social networking among Malaysian undergraduates

  2. To examine the social media identified in objective 1, in order to discover key actors, relationships, their demographics and forms and language of communication and any demographical differences

  3. To develop a preliminary model for purchase intent or actual purchase behaviour based on networks through social media activity

    Undergraduates in a Malaysian HEI were used in this study as they are very typical millennials, and Malaysia was chosen as the authors are teaching in that country.

    Theoretical Underpinning

    The Trajectory of Collective Intelligence

    Collective intelligence can be described as a paradigm allowing actors to solve specific problems, emerging from the collaboration of individuals (Bonabeau, 2009). This paradigm is a mu ltidisciplinary domain because "the study of collective intelligence is considered as a subfield of sociology, communication or behaviour, and computer science or cybernetics" (Scarlat and Maries, 2009: 79). Over the past 30 years, the term "collective intelligence" has held a host of meanings ranging from the behaviour of a "complex adaptive system" (where that system can range from bacteria to ants to humans, e.g., Bloom, 02000), to the distributed knowledge or capability in human systems in which the whole is greater than the sum of the parts (Woolley, 2011). The concept of CI is defined by the literature in different ways. For example, Heylighen (1999: 255) considered "collective intelligence as the ability of a group to solve more problems than its individual members." It has also been defined as the ability of sufficiently large groups of individuals to create an emergent solution for a specific class of problems or tasks (Kornrumpf and Baumol, 2013). Levy (2010) defines CI as a "form of universal, distributed intelligence, which arises from the collaboration and competition of many individuals" whereas Lorenz et al. (2011) define it as "collective intelligence could also be defined as a statistical phenomenon of the 'wisdom of crowds' effect." Collective intelligence did not emerge among groups using computed-mediated communication (CMC) until very recently, suggesting that CI manifests itself differently depending on context (Barlow and Dennis, 2016). Kapetanios (2008: 286) provides a definition for CI when software is used to gather the data as "human-computer systems in which machines enable the collection and harvesting of large amounts of human-generated knowledge, while enabling emergent knowledge." This study acted as a precursor to a "human-computer" approach, the subject of further research by the authors.

    Collective intelligence is very important as support to decision-making, based on collaboration and information exchange between actors (Trigo and Coelho, 2011). The different definitions highlight the role of the intelligent exploitation of information by a group of human actors to resolve a given problem (Malone, 2008). This takes place in an organizational context governed by rules of functioning. It is the general ability of a group to perform a wide variety of tasks (Woolley et al., 2010). The phenomenon is closely related to swarm intelligence, which means collective, largely self-organized behaviour emerging from swarms of social insects (Bonabeau and Meyer, 2001). New forms of CI are constantly emerging because of the Internet, Web 2.0, 3.0 and social media tools, so it is no wonder that interest in the field is rising (Salminen, 2012).

    A wide range of different aspects and components of CI have been studied, at various levels, directly or indirectly, include the following; "social networks of individual and organization, social interaction, familiarity and interpersonal trust" (Chang and Harrington, 2005; Akgun et al., 2005); "group cohesion" (Wang et al., 2006); "diversity, strength of relationship, position in the network, group identification" (Van der Vegt and Bunderson, 2005); "strategic communities, self-organizing innovation networks, self-managing teams" (Rycroft and Kash, 2004); "inter-functional linkages, public institution and policy frameworks, characteristics of the entire sociotechnical network of which a firm is part, informal ties and incubators" (Smilor, 1987; Lumpkin and Ireland, 1988); and "between university and industry" (Rothschild and Darr, 2005; Kreiner and Schultz, 1993); "shared governance, collaborative leadership or distributed leadership" (Bradford and Cohen, 1998; Spillane and Diamond, 2007). The term "wisdom of crowds" was coined by Surowiecki (2005) and describes a phenomenon where, "under certain conditions, large groups can achieve better results than any single individual in the group. For example, the average of several individuals estimates can be accurate even if individual estimations are not" (Surowiecki, 2005). Examining all these different aspects, the researchers decided to use the four CI components, gleaned from the MIT Center for Collective Intelligence, 2017, i.e. form of communication, language of communication, criteria for choice, opinion leaders and influencers.

    In recent years, "The explosion of user-generated content referred to as Web 2.0, including blogs, wikis, video blogs, podcasts, social...

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