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    Modelling the co-dependent diffusion of innovation in two-sided markets : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Marketing at Massey University, Albany Campus, New Zealand
    (Massey University, 2023) Chen, Xing (Alison)
    With the advancement of technology, many innovations, like Electric Vehicles (EV) and contactless payment, are co-dependent. The diffusion of co-dependent innovation requires joint usage from more than one adopting group to enable functionality. For instance, EV owners will not drive their EVs unless they know that there are charging stations along their trips. Contactless payers will not pay with a “waving” or “tapping” of their contactless-enabled cards at the checkout unless they know that merchants accept this payment method. Prior literature terms innovations that are compatible and can be used together as complementary innovations, and those adopted in sequence as contingent innovations (Peterson & Mahajan, 1978). Researchers build models of such innovations based on multi-product growth models or the hardware-software paradigm relying on the operation of the network effects (Bayus, 1987; Bucklin & Sengupta, 1993b; Stremersch et al., 2007). However, these terminologies fail to accurately describe co-dependent innovations, which require uptake by more than one adopting group and will only function with simultaneous use. When there are two distinct adopting groups, the market in which the innovation diffuses is a two-sided market. There is co-dependency between the adopting groups, and thus, between the diffusion path of each innovation. As the diffusion of these co-dependent innovations is yet to be modelled, the current study aims to fill this gap. Using eight years of transaction-based data on a novel payment innovation in a developed western economy, we conceptualise co-dependent diffusion of innovation and examine its properties with three empirical studies. Results from Study 1 (presented in Chapter 2) demonstrate that prior models, including the multi-product Bass model, the model of indirect network effects, and the influx-outflow model proposed for a competitive two-sided market, fail to adequately depict the co-dependent diffusion of innovation. Building on findings from Study 1, Study 2 (presented in Chapter 3) shows that the Bass model with churn rates could be a promising candidate for modelling the co-dependent diffusion of innovation. In the payment innovation context, the churn rate represents the user dropout as a percentage of the current user base. Results reveal that merchants exhibit a higher churn rate than consumers, and the churn rates vary by industry. Simulated churn rates show opposite impacts on the innovation effect and the imitation effect in the diffusion process, where managerial implications can be drawn on tailoring strategies to different adopting groups based on the churn rates aiming to fuel the diffusion. Study 2 also highlights the potential of using churn rates as the proxy for the feedback effects between the adopting groups. As the interaction effect between the adopting groups is established in Study 2, Study 3 (presented in Chapter 4) applies the Vector Error Correction Model (VECM) to account for consumer and merchant usage simultaneously. In the short term, consumer usage increases as a result of the variation in merchants’ usage. The positive response of consumers remains significant in the long run. On the contrary, merchants exhibit decreasing response to the variation in consumers’ usage; thus, only the immediate response is strong and significant in the short run. It is worth noting that, unlike the impact of marketing mix factors on sales that will die down over time, the variation of usage in one adopting group at the early stage of the diffusion could permanently lift the usage of the other group. This provides the first robust insights into the empirical patterns of co-dependency during the diffusion of innovation in two-sided markets and demonstrates how other such markets can be studied in the future. Managers can stimulate usage on one side of the market in the early stage of the innovation growth to leverage the interaction effect between the two sides. As emerged from the current work, an early push of usage on the merchant side may drive the co-dependent diffusion of the innovation in the long run.
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    Forecasting the decline of superseded technologies : a comparison of alternative methods to forecast the decline phase of technologies : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Marketing at Massey University, New Zealand
    (Massey University, 2018) MacRae, Murray Stuart
    An understanding of the economic life of technologies is important for firms, as new technological diffusion often results in rapid erosion of the market value of a firm’s existing technological investments. Little is known about the decline of an older incumbent technology, despite significant effort has been devoted to studying the diffusion of new technologies over the last five decades. There is, it appears, a pro-innovation bias (Rogers, 1995), as theory has a singular focus on the growth side of the substitution phenomenon. Yet to a modern enterprise managing the decline of the older technology may be at least as important as managing the diffusion of the new technology. Consequently, this research takes the first steps towards addressing this gap by investigating how best to predict the decline of an incumbent technology, through an examination of the performance of well-established forecasting methods when applied to the decline phase of a technology life cycle. Interestingly, during the search for historic data it was found that decline series are both rarer than diffusion series, and short, although not as short as diffusion series. Three studies were undertaken; the first study was a competition of four marketing science diffusion models; the Pearl logistic, Gompertz, Bass, and log-logistic models. The second study tested a pooled analogous series approach against the four models from the first study. Twenty-five decline data series were used in those two studies. The final study applied expert judgment to the task using an online panel of 250 UK managers with forecasting experience. These managers undertook expert judgmental forecasting tasks on 12 of the 25 series, spilt over two cue information treatments. Both absolute and comparative measures of accuracy were deployed along with measures to understand bias and variability. The measures were not always in perfect consensus as to the best models in each study; however, the results in aggregate were conclusive. It was found that the Bass and the Pearl logistic were consistently the best marketing science models. However, the online panel of forecasting experts provided a pooled estimate that was competitive with those best marketing science models. Importantly, forecasts from presenting data on decline in tabular form to the panel outperformed the same data presented in graphical form, such that tabular presentation was better than any marketing science model. Also well performed was an analogous series model formed from the average value of a normalised pool of the 25 series, as this approach provided forecasts that were within the range of the two best diffusion models. A straight-line model fitted to the last three data points in the estimation data constantly matched or outperformed all three methods over short horizons. This indicates that simple diffusion models, such as a simple pooled average of available analogous series or even a straight-line model can provide a viable forecast, providing further evidence that simple methods are in general all that is needed to forecast in such situations. Despite laboratory research indicating that individuals are poor at this task, the judgmental study indicates that humans can be successfully used to forecast S-shaped curve trajectories in field trials; however, there are cost and time implications in using a panel that would preclude its use in many situations. References: Rogers, E. M. (1995). Diffusion of innovations (4th ed.). New York, NY: The Free Press.
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    Factors influencing the diffusion of the GIS technology by SBD Qingdao : an [sic] UTAUT approach : a thesis presented in partial fulfillment of the requirements for the degree of Master of Management in Business Information Systems Management at Massey University, Manawatu, New Zealand
    (Massey University, 2011) Sun, Yan; Sun, Yan
    This dissertation examines the roles of various factors of the UTAUT in the adoption of technology by SBD Qingdao to assist in the understanding of technology diffusion within the Chinese swine industry. From research into the Chinese society and swine industry, voluntariness is excluded from the model and education is introduced as a moderating factor. Using a single case study approach, research was conducted on six respondents in influential positions from SBD Qingdao that had direct input into the introduction of the Geographic Information System. Results differed from the original proposed model of UTAUT. It was found that Performance Expectancy (PE), Effort Expectancy (EE) and Social Influence (SI) expected to have direct influence on Behaviour Intention (BI) and Facilitating Conditions (FC) influenced Use Behaviour. PE, SI, FC is expected to be moderated by Experience and Education. EE is moderated by only Education. Age and Gender were not expected to have any moderating effects on the use of new technologies in SBD.