in groups (Valente, 1996; Rice et al., 1990; Sarker, 2006). Prior studies have provided explanations of group-level adoption by computing the arithmetic mean of individual-level adoption of the same IT, assuming that individual membersǯ behavior can be aggregated to explain group behavior (e.g. Jung and Sosik 2003; Lapointe and Rivard 2005). Sarker (2006) found that aggregation of individual-level measures might, however, not be suitable for understanding behavior in a group or social network, and it is now widely accepted that findings at one level of analysis do not generalize well to other levels of analysis, except under very restrictive circumstances (Firebaugh, 1979).
While it is less complicated to understand adoption of IT at the individual level, it is evident that social networks influence individual adoption decisions, and so emphasizing the dynamics at the individual and the social network levels can provide additional insight into IT adoption. Multi-level research addresses the levels of theory, measurement, and analysis required to fully examine research questions. It describes some combination of individuals, groups, organizations, industries, and societies by integrating the micro-domain's focus on individuals with the macro-domain's broader focus, resulting in a richer depiction of the dynamics (Klein et al., 1999). Furthermore, it is well known that relationships that hold at one level of analysis may be stronger or weaker at a different level of analysis or may even reverse direction (Ostroff, 1993).
Following these insights, and as adoption studies in the IS field matures, the assumption is that a solitary individual- or group-level analysis provides an understanding of behaviors occurring at either level only to some extent (Porter, 1996). Taking a multi-level approach may provide additional knowledge in understanding the IT adoption decision made by individuals, social networks, and other units of adoption.
learning, and it operates with three dimensions of competing values. The first dimension relates to organizational focus and differentiates between an internal emphasis on the well-being and development of people in an organization, and an external emphasis on the well-being and development of the organization itself. The second dimension relates to organizational structure and represents the contrast between stability and control as opposed to flexibility and adaptation. The third dimension relates to organizational means and ends with an emphasis on processes and final outcomes. As argued above, evidence in the IS literature suggests that an individual versus social orientation reflects a set of competing forces that are similar to organizational focus. Furthermore, organizational structure can be adapted to IT usage behavior, distinguishing between exploration and exploitation, and the values related to means and ends can be adapted to the objectives of using IT, with a distinction between hedonic and utilitarian objectives. In the following the three identified dilemmas of adoption and use will be described.
4.3.1 Individual and Social Orientation
Contagion studies have established that individuals receptive to social contagion have great influence on the IT diffusion and adoption process (Van den Bulte and Lilien 2001;
Dodds and Watts, 2004) and that the number of relationships an individual has directly affects their opportunities to receive and disseminate information. As described above, individual psychological processes are subject to social influences, and when individuals receive vast amounts of information, conformity may occur. Individuals move from their original cognitive position to a contradictory position (Asch, 1952; Bovard, 1951).
There are several examples in IS of how individual and social influences can shape use of IT in, for example, the individual’s use context (Scheepers and Scheepers, 2004) or within smaller networks (Cambell and Russo, 2003). In general, research on social influence suggests that when social influence is maximized, an individual’s intention to behave independently may be reduced, and when individual intentions to behave are maximized, the emphasis may shift away from the attitudes, beliefs, and behaviors of the group.
Individual adopters are faced with such contradictory cognitive processes when they must make decisions about what information they will react to when adopting and using IT.
Based on the attitudes, beliefs, and behaviors of other people in their social group as well as the media, they are exposed to informational, normative, and competitive influences and thoughts about performance network effects in their adoption decision. There might furthermore be certain individuals with a large knowledge base and a favorable position in the network that transfer knowledge about IT within and between groups and who the majority of the group follow. Finally, individuals need to consider their skills in relation to the use of specific mobile devices and identify other individuals they may observe and learn from. As a mobile device is so personal, most individuals already have a predetermined idea of their needs and wants. In the mobile literature it has been established that individual and social orientation shape adoption and use of mobile technologies. Lu et al. (2008) find that social influences and personal traits, such as individual innovativeness, are potentially important forces in the adoption and use of mobile technology.
4.3.2 Exploration and Exploitation Behavior
Exploration and exploitation behavior has been identified through the organizational behavior literature, where March (1991) was concerned with investigating how individuals balance exploration of new possibilities and exploitation of old certainties. He suggests that exploration involves search, risk taking, experimentation, play, flexibility, discovery, and innovation; whereas exploitation is incremental and involves refinement, choice, production, efficiency, selection, implementation, and execution. The dilemma of balancing exploration and exploitation is revealed in distinctions made between learning about new technologies or refining usage of those that are already known. Exploration is a long-term process, with a risky, uncertain outcome, and exploitation by contrast is short-term, with immediate, relatively certain benefits. Organizations and their members face the problem of allocating resources between exploration and exploitation of IT (Baum et al., 2000, Gupta et al., 2007). The same holds true for consumers possessing
new IT, as they constantly face the choice of exploiting current technologies and services or exploring new technologies and services. Giving too high a priority to exploitation over exploration will cause users to stagnate in technological capability, while overly emphasizing exploration will likely lead to high learning costs with little benefit for practical IT use.
The literature reveals several examples of how exploration and exploitation of IT are conducive to organizational growth. Lee et al. (2003) examine under which conditions exploration of a new, incompatible IT drives growth and find that exploration of new IT is more likely to increase growth when there are a significant number of power users or when a new technology emerges before demand for an established technology escalates.
Kane and Alavi (2007) investigate the effects on exploration and exploitation in organizational learning when introducing IT enabled mechanisms, such as email, knowledge repositories of best practices, and groupware. They find that each of these IT-enabled learning mechanisms enable capabilities that have a distinct effect on the exploration and exploitation learning dynamics in the organization.
4.3.3 Utilitarian and Hedonic Objectives
When investigating the adoption and use of IT, it is necessary to take into consideration the objectives of users and the means through which they sustain themselves and attain their objectives (Georgopoulos and Tannenbaum, 1957). In consumer behavior research a dominant theoretical assumption is based on the Information Processing Model (Bettman, 1979), which regards consumers as logical thinkers who processes the information they receive, rather than merely responding to stimuli, and thus equates the mind to a computer responsible for analyzing information from the environment. In the late 1970’s researchers, however, started questioning the dominance of the Information Processing Model on the grounds that it may neglect important consumption phenomena (e.g.
Olshavsky and Granbois 1979; Sheth 1979), such as playful leisure activities, sensory pleasures, daydreams, aesthetic enjoyment, and emotional responses (Holbrook and Hirschman, 1982). As discussed in Chapter 3: “Adoption and Use of Mobile Devices”
recent research similarly shows a need for distinguishing between utilitarian, productivity-oriented objectives and hedonic, pleasure-oriented objectives (van der Heijden, 2004). Venkatesh and Brown (2001) observe that decisions driving adoption and non-adoption of personal computers are significantly different: adopters are driven by utilitarian, hedonic, and social outcomes while non-adopters are influenced by changes in technology and fear of obsolescence of the adopted technology. Similarly, in mobile studies a correlation between utilitarian and hedonic objectives and mobile adoption and usage increases has been established (e.g. Kim et al., 2002; Lee et al., 2009; Nysveen et al., 2005; Whakefield and Whitten, 2006).
4.3.4 The Integrative Theoretical Perspective
The integrative theoretical approach presented in this chapter lays the groundwork for answering research sub-questions 1 and 2 concerning how a social influence and a competing forces approach can contribute to explaining the adoption and use of app phones.
The aim of presenting the social influence perspective was to place it in the established social network context, as social influences is merely a small part of the social network perspective. This dissertation focuses its research effort in a way that allows for the emergence of the richness and complexity of the social influence approach at the individual and group levels to explain the adoption and use of app phones.
Though the competing forces approach may seem disconnected from the social influence perspective, it provides a different theoretical approach, while simultaneously integrating the main principle of the social influence approach: the question of how people’s thoughts, feelings, and behaviors are influenced by the actual, imagined, or implied presence of others. It therefore seems clear that a social influence approach at the individual and group levels as well as a competing forces approach can contribute to explaining mobile device adoption and use.