Diffusion of innovation (1990–present)

Mahajan, Muller and Bass (1990) review and build on the Bass (1969) model by concluding that adopters of innovation comprise two groups: ‘innovators’,

39 who are externally influenced by the mass media; and ‘imitators’, who are only influenced by word-of-mouth communication. This review further explains that the 1970s added four refinements and extensions to the 1960s models:

market saturation, multi-innovation diffusion, space/time diffusion and multistage diffusion. However, the 1980s produced a vast development on this modelling literature with significant additions being made in the form of parameter considerations, refinements and extensions, and model usage (Mahajan, Muller and Bass 1990).

Ellen, Bearden and Sharma (1991) examined resistance to technology innovations, and concluded that a person's perceived ability to use a product successfully affects their evaluative and behavioural response to that product.

In addition, the level of satisfaction experienced through an existing behaviour increases resistance to and reduces likelihood of adopting an alternative (Ellen et al., 1991). Moore and Benbasat (1991) developed an eight-point scale (Voluntariness, Relative advantage, Compatibility, Image, Ease of Use, Result Demonstrability, Visibility, and Trialability) to measure the perceptions of adopting information technology (IT) innovation. However, much of the success of a sequential strategy comes from the producer’s ability to commit to future products and prices; when this is not the case, sequential selling does not facilitate new product designs to alleviate any possible

cannibalisation.

Martin Bauer (1995) suggests that barriers to technology adoption can also come at the individual level and that human actors can present as resistant, innovators or observers depending on the situation faced. For example, the introduction of IT into business from the 1960s through to the 1980s was resisted by top management and bottom management but innovated by middle management. However, with regards to the introduction of new manufacturing methods, the mid-level employees were more likely to resist (Bauer, 1995).

Moore (1999) built on Rogers’s (1983) work to argue that a ‘chasm’ exists between early adopters and the early majority, and that this chasm is the

40 reason why many new innovations, while popular with approximately 15% of the population, fail to convince the mainstream to adopt them. Rodger’s (1983) did not share this view, instead claiming that the population categories form a continuum from adopters to majority. The motivations, interests and needs of the early and later adopter categories are significantly different and thus the complete adoption of an innovation through to the later majority is not an automatic process. Since Rogers’s (1983) death in 2004, many academics have questioned the influence of the chasm; however, Libai, Mahajan, and Muller (2015) support the chasm theory and suggest that it may be more prevalent than Moore (1999) first claimed.

Aggarwal, Cha and Wilemon (1998) investigated the barriers to the adoption of really new products to conclude that ‘surrogate consumers’ – namely, agents retained by a customer to guide, direct and/or transact market place activities (Solomon, 1986, p8) – provide many of the solutions to such barriers.

The literature on innovation adoption has relied primarily on Rogers’s (1983) classification of adopter groups (from innovators to laggards) to identify

consumers’ adoption potential, suggesting that new innovations should first be targeted at the ‘innovators’ and then, moving down the list, at the other less innovative groups in sequence. Mick and Fournier (1998) challenge this theory, stating that it is an oversimplification to characterise the late majority onwards as laggard and/or technology resisters, particularly as many of these consumers have already adopted the previous generations of products. The implications of a person’s age when adopting new technology have been considered by Venkatesh and Davis (2000), who conclude that younger people’s adoption decisions are influenced by attitudes towards using the technology, whereas for older people, perceived behavioural control and, to a lesser extent, subjective norms are the influence. In 2001, Lyytinen and Damsgaard produced a paper entitled ‘What’s wrong with the diffusion of innovation theory?’ which looked at complex and networked technology products. Their conclusion was that diffusion of innovation theory does not offer adequate constructs to account for collective adoption behaviours such

41 as standards, critical mass, network externalities, sunk costs and path

dependence.

Venkatesh, Morris, Davis and Davis (2003) reviewed the following eight existing IT-related adoption models: Theory of Reasoned Action (TRA), Technology Acceptance Model (TAM), Motivational Model (MM), Theory of Planned Behaviour (TPB), Model of PC Utilisation (MPCU), Innovation Diffusion Theory (IDT) and Social Cognitive Theory (SCT). Measuring data from four industries – entertainment, telecommunications, banking and public administration – they presented a unified model called the Unified Theory of Acceptance and Use of Technology (UTAUT). UTAUT was empirically found to outperform the existing eight models by explaining 70% of consumer behavioural intention or usage in a professional industrial context, and thus helping industry managers better understand the likelihood of success of future technology introductions, and the relevant training and internal marketing communications required.

Herzenstein et al. (2007) investigated the influence of consumers’

self-regulation systems and the prominence of risks when adopting new and really new products. They suggest that when the risks associated with a really new product are not specified to consumers, promotion-focused consumers have higher purchase intentions than do prevention-focused consumers. However, when the judgmental context makes the risks salient, prevention- and

promotion-focused participants are equally unlikely to purchase the product (Herzenstein, Posavac and Brakus, 2007).

Stremerch, Muller and Peres (2010) identified a paradox in the literature prior to 2009. From one viewpoint, the diffusion literature concludes that more recently introduced products exhibit a faster diffusion than do older products.

However, the contradicting viewpoint from the technology generation literature is that the growth rate when measured using diffusion parameters remains constant across generations. Their study sought to resolve this paradox by examining 39 technology generations among 12 products, including

televisions, disk drives, personal computers and audio systems, all of which

42 are relevant to this thesis. Stremerch et al. (2009) assert that the general diffusion processes do not change across generations, and that it is in fact time (i.e. the passing of time) that is the most important factor instead of next generational changes. They support this by stating that new generations start to diffuse more quickly but still exhibit a similar overall growth process

(Stremerch et al., 2010).

Stremerch et al. (2010) argue that any company that launches

next-generation innovations to the market needs to scale up manufacturing and marketing resources at an ever-increasing speed for each product generation leap they make. However, a shorter planning time does not guarantee a quicker overall diffusion process. Therefore, industry planners should not simply believe that faster take-off rates will provide earlier sales peaks and/or an overall faster growth and adoption of their new products. Stremerch et al.

(2010) also warn of the dangers of impending commercial failure when consumers ‘leapfrog’ a technology generation due to the fact that it took far longer to take off than did the previous generation. Most often the smart move in this situation is to withdraw support for the failing generation and channel the innovative energy into the next generation. Interestingly, the conclusions drawn by Stremerch et al. (2010) appear to further contradict the findings on upgrading published by Huh and Kim (2008), who argue that it is the usage behaviour of the current generation that exerts more influence on the adoption of the next generation, rather than the passing of time.

Cui, Bao and Chan (2009) draw a connection between adoption, upgrading and disposal considerations of existing products, and propose that

accelerated technology innovations lead to shorter product lifecycles. They claim that consumers often face the dilemma of choosing between keeping the existing product and upgrading to a new version, and may enact certain coping strategies to deal with the stress and uncertainty surrounding this decision-making. Cui et al. (2009) discuss the influence of three coping strategies – refusal, delay and extended decision-making – and propose a measure of the delay strategies using statements such as ‘I will not buy a new product until my existing one fails’, ‘I will not buy innovative products until the

43 existing one becomes outdated’, and ‘I tend to delay adopting new products because they may become outdated soon’ (Cui et al., 2009, p155). These authors also state that ‘consumers need to decide whether to keep using the existing product or upgrade. There is no evidence that the same adoption pattern will repeat and we have little knowledge about how consumers make such “upgrade” decisions’ (Cui et al., 2009, p111).

Finally, MacVaugh and Schiavone (2010) produced a review paper on new technology products entitled ‘Limits to the diffusion of innovation’, which investigated both non-adoption of new technology and the more common researched topic of adoption via new replacing old technology. The paper also presents an integrated model of nine factors that shape innovation adoption:

three classified as technology related (utility, complexity and complementary);

three regarding social structure (context, orientation and contagion); and three related to learning context (capacity, capability and costs). MacVaugh and Schiavone (2010) found that all three conditions affect innovation diffusion within a consumer’s individual domain context.

In the context of this study this is relevant research as it explores the reasons why a consumer may or may not adopt – that is, purchase new technology – given the prevalence of technology being produced and shorter product lifecycles being experienced.

44 2.1.5 Section summary

In setting the background context for this research, this section has briefly reviewed foundational academic research from the past 50 years. Although these studies focus on first-time adoptions, many of the conclusions identified are still relevant today and thus accordingly are incorporated into this thesis in its investigation of the drivers of upgrade behaviour. The relevant papers include: ‘Media exposure and word of mouth’ (Fort and Woodcock 1960, Mansfield 1961), ‘Opinion leaders’ (Rogers 1962) and ‘Technology adoption’

(Davis, et al., 1989). Interestingly, Norton and Bass (1987) appear to be the first authors to suggest the crossover with the next generation or ‘upgrading’

work reviewed later in this section. This work published 27 years ago started a discourse focused on the drivers product replacement. The discussion has been further developed by Huh and Kim (2008), who sought to establish associations between early adopters and early upgrading behaviour, and concluded that the use behaviour of features drives upgrade intent. In

contrast, Stremerch, Muller and Peres (2010) state that the passage of time is the most important factor. Even high-volume, ramped-up marketing activity designed to facilitate a faster innovation diffusion take-off will not speed up the overall diffusion rate. Finally, Cui et al. (2009) argue that a greater connection with upgrading is required, and that repeat adoption patterns cannot be

accurately predicted as not enough is known about ‘upgrading’ decisions.

45 2.2 PRODUCT REPLACEMENT, NEXT GENERATION AND UPGRADING

This section will investigate the literature on product replacement, next-generation products and upgrading.