Adoption dynamics of increasing-return technologies in systemic contexts

Many systemic, complex technologies have been suggested to exhibit increasing returns to adoption, whereby the initial increase in adoption leads to increasing experience with the technology, which drives technological improvements and use, subsequently leading to further adoption. In addition, in the systemic context, mimetic behavior may lend support to increasing returns as technology adoption is witnessed among other agents in the systemic context. Finally, inter-dependencies in the systemic context also sensitize the adoption behavior to fundamental changes in technology provisioning, and this may lend support for the increasing returns type of dynamics in adoption. Our empirical study examines the dynamics of organizational technology adoption when technology is provisioned by organizations in another sub-system in a systemic context. We hypothesize that innovation, imitation, and technological change effects are present in creating increasing returns in the systemic context. Our empirical setting considers 24 technologies represented by 2282 data points in the computer industry. Our results provide support for our prediction that imitation effects are present in creating increasing returns to adoption. We further discuss the managerial and research implications of our results.

Drivers of RFID adoption in the automotive industry

The automotive industry is confronted with increasing competition, leading to higher cost pressures and the demand to optimize production processes and value chains. Here the RFID technology promises to improve a range of processes in logistics and manufacturing. Despite its promising potential in the automotive industry, RFID has not yet made a decisive step from pilots to real-life implementations in the supply chain. Building on existing models of technology adoption, we analyze RFID adoption dynamics in the automotive industry. Building on existing IOS adoption models tailored to RFID specifics and based on ten semi-structured interviews with OEMs and suppliers, we evaluate main drivers of RFID adoption in the automotive industry. Our key findings are that the use of a coercive approach by the OEM could be redundant because of the market-driven RFID adoption among many suppliers. Furthermore, suppliers implementing RFID can now gain an early mover competitive advantage by developing higher trust in their relationship with the OEM as well as accumulating unique expertise in this area.

Drivers of adoption for successive generation of high-tech products

To understand the diffusion of high technology products such as PCs, digital cameras and DVD players it is necessary to consider the dynamics of successive generations of technology. From the consumer’s perspective, these technology changes may manifest themselves as either a new generation product substituting for the old (for instance digital cameras) or as multiple generations of a single product (for example PCs). To date, research has been confined to aggregate level sales models. These models consider the demand relationship between one generation of a product and a successor generation. However, they do not give insights into the disaggregate-level decisions by individual households – whether to adopt the newer generation, and if so, when. This paper makes two contributions. It is the first large scale empirical study to collect household data for successive generations of technologies in an effort to understand the drivers of adoption. Second, in contrast to traditional analysis in diffusion research that conceptualizes technology substitution as an “adoption of innovation” type process, we propose that from a consumer’s perspective, technology substitution combines elements of both adoption (adopting the new generation technology) and replacement (replacing generation I product with generation II). Key Propositions In some cases, successive generations are clear “substitutes” for the earlier generation (e.g. PCs Pentium I to II to III ). More commonly the new generation II technology is a “partial substitute” for existing generation I technology (e.g. DVD players and VCRs). Some consumers will purchase generation II products as substitutes for their generation I product, while other consumers will purchase generation II products as additional products to be used as well as their generation I product. We propose that substitute generation II purchases combine elements of both adoption and replacement, but additional generation II purchases are solely adoption-driven process. Moreover, drawing on adoption theory consumer innovativeness is the most important consumer characteristic for adoption timing of new products. Hence, we hypothesize consumer innovativeness to influence the timing of both additional and substitute generation II purchases but to have a stronger impact on additional generation II purchases. We further propose that substitute generation II purchases act partially as a replacement purchase for the generation I product. Thus, we hypothesize that households with older generation I products will make substitute generation II purchases earlier. Methods We employ Cox hazard modeling to study factors influencing the timing of a household’s adoption of generation II products. A separate hazard model is conducted for additional and substitute purchases. The age of the generation I product is calculated based on the most recent household purchase of that product. Control variables include size and income of household, age and education of decision-maker. Results and Implications Our preliminary results confirm both our hypotheses. Consumer innovativeness has a strong influence on both additional purchases and substitute purchases. Also consistent with our hypotheses, the age of the generation I product has a dramatic influence for substitute purchases of VCR/DVD players and a strong influence for PCs/notebooks. Yet, also as hypothesized, there was no influence on additional purchases. This implies that there is a clear distinction between additional and substitute purchases of generation II products, each with different drivers. For substitute purchases, product age is a key driver. Therefore marketers of high technology products can utilize data on generation I product age (e.g. from warranty or loyalty programs) to target customers who are more likely to make a purchase.

(Not) the Twitter election: The dynamics of the #ausvotes conversation in relation to the Australian media ecology

This paper draws on a larger study of the uses of Australian user-created content and online social networks to examine the relationships between professional journalists and highly engaged Australian users of political media within the wider media ecology, with a particular focus on Twitter. It uses an analysis of topic based conversation networks using the #ausvotes hashtag on Twitter around the 2010 federal election to explore the key themes and issues addressed by this Twitter community during the campaign, and finds that Twitter users were largely commenting on the performance of mainstream media and politicians rather than engaging in direct political discussion. The often critical attitude of Twitter users towards the political establishment mirrors the approach of news and political bloggers to political actors, nearly a decade earlier, but the increasing adoption of Twitter as a communication tool by politicians, journalists, and everyday users alike makes a repetition of the polarisation experienced at that time appear unlikely.

Adoption of innovative products on Australian road infrastructure projects

Product innovation is an important contributor to the performance of infrastructure projects in the construction industry. Maximizing the potential for innovative product adoption is a challenging task due to the complexities of the construction innovation system. A qualitative methodology involving interviews with major construction project stakeholders is employed to address the research question: ‘What are the main obstacles to the adoption of innovative products in the road industry?’ The characteristics of six key product innovation obstacles in Australian road projects are described. The six key obstacles are: project goal misalignment, client pressures, weak contractual relations, lack of product trialling, inflexible product specifications and product liability concerns. A snapshot of the dynamics underlying these obstacles is provided. There are few such assessments in the literature, despite the imperative to improve construction innovation rates globally in order to deliver road infrastructure projects of increasing size and complexity. Key obstacles are interpreted through an open innovation construct, providing direction for policy to enhance the uptake of innovation across the construction product supply network. Early evidence suggests the usefulness of an open innovation construct that integrates three conceptual lenses: network governance, absorptive capacity and knowledge intermediation, in order to interpret product adoption obstacles in the context of Australian road infrastructure projects. The paper also provides practical advice and direction for government and industry organizations that wish to promote the flow of innovative product knowledge across the construction supply network.

Accounting regulation for charities : international responses to IFRS adoption

This paper examines charity regulatory systems, including accounting standard setting, across five jurisdictions in varying stages of adoption of International Financial Reporting Standards, and identifies the challenges of this process. Design/methodology/approach Using a regulatory space approach, we rely on publicly available archival evidence from charity regulators and accounting standard setters in five common-law jurisdictions in advanced capitalist economies, all with vibrant charity sectors: United Kingdom, United States of America, Canada, Australia and New Zealand. Findings The study reveals the importance of co-operative interdependence and dialogue between charity regulators and accounting standard setters, indicating that jurisdictions with such inter-relationships will better manage the transition to IFRS. It also highlights the need for those jurisdictions with not-for-profit or charity-specific accounting standards to reconfigure those provisions as IFRSs are adopted. Research limitations/implications The study is limited to five jurisdictions, concentrating specifically on key charity regulators and accounting standard setters. Future research could widen the scope to other jurisdictions, or track changes in the jurisdictions longitudinally. Practical implications We provide a timely international perspective of charity regulation and accounting developments for regulators, accounting standard setters and charities, specifically of regulatory responses to IFRS adoption. Originality/value: The paper contributes fresh insights into the dynamics of charity accounting regulation in an international context by using regulatory space as an organising framework. While accounting regulation literature provides a rich interpretation of regulatory issues within the accounting arena, little attention has been paid to charity accounting regulation.

A systemic view of innovation adoption in the Australian beef industry

Significant investments in developing technological innovations have been made in the Australian beef industry but with low adoption rates. By modelling the key variables and their interactions in the innovation adoption process, this research seeks to demonstrate the complexity and dynamics of the process. This research uses causal loop modelling and develops a holistic model of the current innovation adoption system in the Australian beef industry to show the complexity of dynamic interactions among multiple variables. It is suggested that innovation adoption is such an extremely complex issue, and we need to shift our views on this issue from a paradigm of linear thinking to systems thinking. Innovation adoption is more likely to be enhanced based on a full understanding of the complexity and dynamics of the system as a whole. The paper demonstrates to practitioners and developers of innovation the multiple variables and interactions impacting innovation adoption.

Enterprise resource planning or enterprise resource plotting? Technological, organisational and inter-organisational aspects of enterprise resource planning package adoption and diffusion

Those in organisations tend to adopt new technologies as a way to improve their functions, reduce cost and attain best practices. Thus, technology promoters (or vendors) work along those lines in order to convince adopters to invest in those technologies and develop their own organisations profit in return. The possible resultant ‘conflicts of interest’ makes the study of reasons behind IT diffusion and adoption an interesting subject. In this paper we look at IT diffusion and adoption in terms of technology (system features), organisational aspects (firm level characteristics) and inter-organisational aspects (market dynamics) in order to see who might be the real beneficiaries of technology adoption. We use ERP packages as an example of an innovation that has been widely diffused and adopted for the last 10 years. We believe that our findings can be useful to those adopting ERP packages as it gives them a wider view of the situation.

Coalition formation during technology adoption

The Technology Acceptance Model (TAM) is a prominent framework that addresses the challenge of organisations to understand and promote the factors that lead to acceptance of new technologies. Nevertheless, our understanding of one of the model's key variables – social influence – remains limited. Drawing upon earlier studies that address the role of referent individuals to technology acceptance, this paper introduces the notion of ‘coalition’ as a social group that can affect the opinion of other members within an organisation. Our empirical study centres on an organisation that has recently decided to introduce Big Data into its formal operations. Through a unique empirical approach that analyses sentiments expressed by individuals about this technology on the organisation's online forum, we demonstrate the emergence of a central referent, and in turn the dynamics of a coalition that builds around this referent as the attitudes of individuals converge upon the Big Data issue. Our paper contributes to existing TAM frameworks by elaborating the social influence variable and providing a dynamic lens to the technology acceptance process. We concurrently offer a methodological tool for organisations to understand social dynamics that form about a newly introduced technology and accelerate its acceptance by employees.

Weed dynamics as influenced by tillage system, sowing time and weed competition duration in dry-seeded rice

Cultural practices alter patterns of crop growth and can modify dynamics of weed-crop competition, and hence need to be investigated to evolve sustainable weed management in dry-seeded rice (DSR). Studies on weed dynamics in DSR sown at different times under two tillage systems were conducted at the Agronomic Research Farm, University of Agriculture, Faisalabad, Pakistan. A commonly grown fine rice cultivar 'Super Basmati' was sown on 15th June and 7th July of 2010 and 2011 under zero-till (ZT) and conventional tillage (CONT) and it was subjected to different durations of weed competition [10, 20, 30, 40, and 50 days after sowing (DAS) and season-long competition]. Weed-free plots were maintained under each tillage system and sowing time for comparison. Grassy weeds were higher under ZT while CONT had higher relative proportion of broad-leaved weeds in terms of density and biomass. Density of sedges was higher by 175% in the crop sown on the 7th July than on the 15th June. Delaying sowing time of DSR from mid June to the first week of July reduced weed density by 69 and 43% but their biomass remained unaffected. Tillage systems had no effect on total weed biomass. Plots subjected to season-long weed competition had mostly grasses while broad-leaved weeds were not observed at harvest. In the second year of study, dominance of grassy weeds was increased under both tillage systems and sowing times. Significantly less biomass (48%) of grassy weeds was observed under CONT than ZT in 2010; however, during 2011, this effect was non-significant. Trianthema portulacastrum and Dactyloctenium aegyptium were the dominant broad-leaved and grassy weeds, respectively. Cyperus rotundus was the dominant sedge weed, especially in the crop sown on the 7th July. Relative yield loss (RYL) ranged from 3 to 13% and 7 to16% when weeds were allowed to compete only for 20 DAS. Under season-long weed competition, RYL ranged from 68 to 77% in 2010 and 74 to80% in 2011. The sowing time of 15th June was effective in minimizing weed proliferation and rectifying yield penalty associated with the 7th July sowing. The results suggest that DSR in Pakistan should preferably be sown on 15th June under CONT systems and weeds must be controlled before 20 DAS to avoid yield losses. Successful adoption of DSR at growers' fields in Pakistan will depend on whether growers can control weeds and prevent shifts in weed population from intractable weeds to more difficult-to-control weeds as a consequence of DSR adoption.

Trade Liberalization, Heterogeneous Firms and Industrial Dynamics

This master thesis studies how trade liberalization affects the firm-level productivity and industrial evolution. To do so, I built a dynamic model that considers firm-level productivity as endogenous to investigate the influence of trade on firm’s productivity and the market structure. In the framework, heterogeneous firms in the same industry operate differently in equilibrium. Specifically, firms are ex ante identical but heterogeneity arises as an equilibrium outcome. Under the setting of monopolistic competition, this type of model yields an industry that is represented not by a steady-state outcome, but by an evolution that rely on the decisions made by individual firms. I prove that trade liberalization has a general positive impact on technological adoption rates and hence increases the firm-level productivity. Besides, this endogenous technology adoption model also captures the stylized facts: exporting firms are larger and more productive than their non-exporting counterparts in the same sector. I assume that the number of firms is endogenous, since, according to the empirical literature, the industrial evolution shows considerably different patterns across countries; some industries experience large scale of firms’ exit in the period of contracting market shares, while some industries display relative stable number of firms or gradually increase quantities. The special word “shakeout” is used to describe the dramatic decrease in the number of firms. In order to explain the causes of shakeout, I construct a model where forward-looking firms decide to enter and exit the market on the basis of their state of technology. In equilibrium, firms choose different dates to adopt innovation which generate a gradual diffusion process. It is exactly this gradual diffusion process that generates the rapid, large-scale exit phenomenon. Specifically, it demonstrates that there is a positive feedback between firm’s exit and adoption, the reduction in the number of firms increases the incentives for remaining firms to adopt innovation. Therefore, in the setting of complete information, this model not only generates a shakeout but also captures the stability of an industry. However, the solely national view of industrial evolution neglects the importance of international trade in determining the shape of market structure. In particular, I show that the higher trade barriers lead to more fragile markets, encouraging the over-entry in the initial stage of industry life cycle and raising the probability of a shakeout. Therefore, more liberalized trade generates more stable market structure from both national and international viewpoints. The main references are Ederington and McCalman(2008,2009).

Expanding adoption of drinking water treatment systems in developing countries: a case study from Tamil Nadu, India

Household-level water treatment and safe storage systems (HWTS) are simple, local, user-friendly, and low cost options to improve drinking water quality at the point of use. However, despite conclusive evidence of the health and economic benefits of HWTS, and promotion efforts in over 50 countries in the past 20 years, implementation outcomes have been slow, reaching only 5-10 million regular users. This study attempts to understand the barriers and drivers affecting HWTS implementation. Using a case study example of a biosand filter program in southern India, system dynamics modelling is shown to be a useful tool to map the inter-relationships of different critical factors and to understand the dissemination dynamics. It is found that the co-existence of expanding quickly and achieving financial sustainability appears to be difficult to achieve under the current program structure.

Simulating early adoption of alternative fuel vehicles for sustainability

We quantify the conditions that might trigger wide spread adoption of alternative fuel vehicles (AFVs) to support energy policy. Empirical review shows that early adopters are heterogeneous motivated by financial benefits, environmental appeal, new technology, and vehicle reliability. A probabilistic Monte Carlo simulation model is used to assess consumer heterogeneity for early and mass market adopters. For early adopters full battery electric vehicles (BEVs) are competitive but unable to surpass diesels or hybrids due to purchase price premium and lack of charging availability. For mass adoption, simulations indicate that if the purchase price premium of a BEV closes to within 20% of an in-class internal combustion engine (ICE) vehicle, combined with a 60% increase in refuelling availability relative to the incumbent system, BEVs become competitive. But this depends on a mass market that values the fuel economy and CO2 reduction benefits associated with BEVs. We also find that the largest influence on early adoption is financial benefit rather than pro-environmental behaviour suggesting that AFVs should be marketed by appealing to economic benefits combined with pro-environmental behaviour to motivate adoption. Monte Carlo simulations combined with scenarios can give insight into diffusion dynamics for other energy demand-side technologies. © 2012 Elsevier Inc.

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.