Adoption Curve Research Articles

Balancing technological innovation with waste burden minimization: An examination of the global lighting industry

Technological innovation has the promise to catalyze reduced environmental impacts through higher energy efficiency, product design that promotes reuse, and use of benign materials. However, as new technologies emerge, it is easy to lose sight of the environmental burden associated with technology transition and premature replacement of technology. Though product turnover can lead to gains in energy efficiency, it also can contribute to the creation of expanded and diversified waste streams.The market for residential lighting products continues to rapidly evolve, with new LED products emerging each year. Technology change in the lighting industry has unintended consequences, including new waste streams associated with the obsolete products. This paper examines the waste burden that occurs as a consequence of technology change when lighting products are replaced or upgraded. To understand the impact of residential lighting replacement, the authors used life cycle assessment to evaluate LED products. We defined three personas representing various typical technology adopters within the product adoption curve to quantify the volume and change rate of lighting products. The personas have also been used to estimate the waste impact for larger populations, and the results show that while an innovator’s waste burden is lowest amongst the three personas, a non-trivial waste burden accompanies the transition to LED lighting products for all three personas. The results quantify the waste burden of high performance lighting and further motivate the development and implementation of recycling programs and policies to prevent waste diverted to landfills by consumers. The results also confirm that attention must be paid to how to reduce the waste burden of LED lighting products through improved design and lighting as a service models.

Compartmental diffusion modeling: Describing customer heterogeneity & communication network to support decisions for new product introductions

Following the introduction of the well-known Bass model and its variations to model adoption of a new product, compartmental modeling has emerged, in which the adoption pattern is modeled at the level of each compartment (or customer segment). Compartmental models add spatial and psychographic segments to Bass-type models which can allow modelers to approximate the heterogeneity, clustering and communication network of customers, while retaining an analytical structure. These models have been mainly explored in the context of demand forecasting and describing customer behavior, but their suitability as a scalable modeling tool to support large scale marketing and/or operational decision making is not explored. In this paper, we propose a flexible compartmental model and assess its suitability in terms of use of data, adherence to micro-level customer behavior, and use in large scale decision making. We show the merits of the CDE model by carrying out an extensive simulation study and also estimation on data on boradband adoption. We find that compartmental models result in estimates that are drastically less biased and can predict the shape of the adoption curve significantly better than what can be achieved by the Bass model. Even though these models can be scalable to capture large number of segments, we show that these improvements can still be observed with even a small number of segments (in contrast to what has been hypothesized in the literature), and are not sensitive to errors in the underlying process of customer segmentation. Therefore, the analytical structure of compartmental models can be further explored to further support large scale marketing and operational decision making, as they do not inherit the major shortcomings of Bass-type models while they capture the effect of customer clustering and interactions. Even though the focus of this paper is on new product adoption, compartmental modeling can also be successfully utilized in diverse physical, biological, and social settings that are governed by similar dynamics.

The absorption and usage of cloud accounting technology by accounting firms in Cape Town for services provided to their clients

Cloud computing is one of the latest technological innovations and offers organizations of all sizes new and unique opportunities and challenges. Many firms worldwide have, however, become used to running their businesses in the traditional way and seem to be resisting the adoption of this new technology. This study explores the adoption and usage of cloud technology by accounting firms in South Africa for the services provided to their clients. Online questionnaires were completed by 27 owners or managers of accounting firms in Cape Town and descriptive and inferential statistical methods were used to analyse the results. It emerged that significant awareness exists of cloud accounting technology by accounting firm managers and owners. Smaller firms seem to be more positive toward the technology and also more agile and capable of deploying it than medium-large firms. The medium-large firms could be classified as the late majority and laggards on the technology adoption curve while smaller firms could be classified as innovators, early adopters and the early majority. Marketers of cloud accounting products need to consider the firm’s size, as well as the industrial decision-making process and perceived risk involved when designing their marketing campaigns.

Future trajectories of urban drainage systems: A simple exploratory modeling approach for assessing socio-technical transitions

In this work, we present a novel approach to explore future trajectories in urban drainage systems, emphasizing the adoption and implementation of sustainable ‘nature-based’ stormwater management strategies. The focus is on the development and long-term assessment of socio-technical pathways to create a multifunctional stormwater system at the city scale. The innovation is to identify and represent the socio-technical pathways by means of adoption curves for such transition processes. We combine urban planning policies and state-of-the-art urban engineering approaches with societal aspects and analyze them with traditional biophysical models (hydrologic-hydraulic sewer modeling). In doing so, different pathways from a current to a future system state are investigated under a variety of political, population and climate scenarios. Results allow for strategy screening by addressing the spatial and temporal implementation of decentralized stormwater control measures, to enable a successful transition to a sustainable future city. The model is applied to an ongoing transition of Kiruna, a city in Sweden, considering 36 different future trajectories over a transition period of 23 years. Results show that the trajectory of raingarden implementation under a sustainability policy can alleviate the adverse effects of urbanization (growth scenario). While this trajectory resulted in, for example, nearly the same sewer surcharge performance as that characterized by declining urbanization (stagnation) and a business-as-usual policy (with expected raingarden uptake rates approximately one-third lower), significantly better ecological performances (e.g. runoff treatment ratios up to 50%) are achieved.

In with the new? Generational differences shape population technology adoption patterns in the age of self-driving vehicles

Abstract With rapid growth of self-driving vehicle technologies, policymakers and industry are actively engaging the public to understand attitudes toward smart mobility. As public officials explore implementing connected systems, they may find diverse reactions. We present an important insight using precise technology adoption curves for three age groups within a major initiative in the United Kingdom, going beyond theoretical expectations. Specifically, the adaptation of self-driving cars reflects the patterns of adaptation to previous technologies. Furthermore, older participants were more likely to be late adopters of the technology than younger participants. Implications from these insights offer the opportunity to enhance public engagement and optimize the implementation of such systems, thereby maximizing population benefits.

Sustainable Diffusion of Inter-Organizational Technology in Supply Chains: An Approach to Heterogeneous Levels of Risk Aversion

This paper develops a model to analyze inter-organizational technology adoption in a supply chain. While the basic model is general, this study is motivated by several cases of inter-organizational technology adoption in supply chains. The proposed model in this study considers firms on both levels of the supply chain, namely, supplier firms and buyer firms. These individual firms’ thresholds for adoption should be considered by other firms’ decisions within a network, together with their own organizational attributes. The heterogeneity across the population should be allowed. That is, individual firms make a decision for adopting the technology at different times due to their different network sizes, prior beliefs, and amounts of information observed. The main finding is that this uncertainty decreases as other suppliers adopt the technology, and information about their experiences becomes available. In addition, at any given time, an estimate of the benefit to a supplier depends on the number of supplier firms and on the number of buyer firms that have already adopted the technology. Thus, we seek to capture this dependence and analyze its effect on the adoption of a new inter-organizational technology. The next step is to embed the firm-level adoption model into a population model. The model includes various types of heterogeneity in the population model to capture the factors affecting the speed of diffusion. This allows us to derive an adoption curve that is specified by the accumulated fraction of firms that have adopted the technology in or before any given period. The population model allows us to consider the effect of several strategies observed in practice and numerical experiments yielding many managerial implications in this area.

Extent of Adoption of House terrace Cultivation of Urban dwellers of Thiruvananthapuram Corporation, Kerala

This paper assesses the adoption of recommended practices for house terrace cultivation in Thiruvananthapuram Corporation of Kerala by the urban dwellers. Majority of the urban dwellers came under medium level of adoption of recommended practices for house terrace cultivation of Kerala Agricultural University. With respect to Rogers’ adoption curve, categorisation of respondents was done on the basis of adoption of the recommended practices for house terrace cultivation of Kerala Agricultural University The results revealed that there was high percentage of respondents in the early adopter category.

Product diffusion through on-demand information-seeking behaviour

Most models of product adoption predict S-shaped adoption curves. Here we report results from two country-scale experiments in which we find linear adoption curves. We show evidence that the observed linear pattern is the result of active information-seeking behaviour: individuals actively pulling information from several central sources facilitated by modern Internet searches. Thus, a constant baseline rate of interest sustains product diffusion, resulting in a linear diffusion process instead of the S-shaped curve of adoption predicted by many diffusion models. The main experiment seeded 70 000 (48 000 in Experiment 2) unique voucher codes for the same product with randomly sampled nodes in a social network of approximately 43 million individuals with about 567 million ties. We find that the experiment reached over 800 000 individuals with 80% of adopters adopting the same product—a winner-take-all dynamic consistent with search engine driven rankings that would not have emerged had the products spread only through a network of social contacts. We provide evidence for (and characterization of) this diffusion process driven by active information-seeking behaviour through analyses investigating (a) patterns of geographical spreading; (b) the branching process; and (c) diffusion heterogeneity. Using data on adopters' geolocation we show that social spreading is highly localized, while on-demand diffusion is geographically independent. We also show that cascades started by individuals who actively pull information from central sources are more effective at spreading the product among their peers.

Data-Driven Investment Decision-Making: Applying Moore's Law and S-Curves to Business Strategies

This paper introduces a method for linking technological improvement rates (i.e. Moore’s Law) and technology adoption curves (i.e. S-Curves). There has been considerable research surrounding Moore’s Law and the generalized versions applied to the time dependence of performance for other technologies. The prior work has culminated with methodology for quantitative estimation of technological improvement rates for nearly any technology. This paper examines the implications of such regular time dependence for performance upon the timing of key events in the technological adoption process. We propose a simple crossover point in performance which is based upon the technological improvement rates and current level differences for target and replacement technologies. The timing for the cross-over is hypothesized as corresponding to the first ‘knee’ in the technology adoption ‘S-curve’ and signals when the market for a given technology will start to be rewarding for innovators. This is also when potential entrants are likely to intensely experiment with product-market fit and when the competition to achieve a dominant design begins. This conceptual framework is then back-tested by examining two technological changes brought about by the internet, namely music and video transmission. The uncertainty analysis around the cases highlight opportunities for organizations to reduce future technological uncertainty. Overall, the results from the case studies support the reliability and utility of the conceptual framework in strategic business decision-making with the caveat that while technical uncertainty is reduced, it is not eliminated.

Interview with Deborah DiSanzo of IBM Watson Health

Interview with Deborah DiSanzo of IBM Watson Health

The smartphone as your follower: The role of smartphone literacy in the relation between privacy concerns, attitude and behaviour towards phone-embedded tracking

Previous research on the collection of personal information in order to make tailor made advertising and services possible shows that the more overt the collection is, the more users will resist and try to avoid it. However, this research has always focussed on third parties, like apps and websites, collecting this information, while the smartphones on which these third parties operate also collect information. Based on the Psychological Ownership Theory and the Diffusion of Innovations Theory a survey (N = 924) has shown that the more privacy concerns users experience, the more negative their attitudes are towards the collection of location data by their smartphones, and that they adjust the settings of their smartphones in order to prevent being tracked. Users' earlier position on the adoption curve, and with that their smartphone literacy, decreases the strength of the connection between privacy concerns and attitude. It is important for the companies that are responsible for location tracking to help users feeling in control and to acknowledge that people own their data, not the companies who bought their data.

(Invited) Intelligent Innovation in the Water Sector. Opportunities and Challenges in Sensor Design for the Drinking and Wastewater Markets

The water and wastewater industries face many challenges in providing safe drinking water, sanitary services and environmental protection to the public. The dynamic nature of our water systems along with new regulations, emerging contaminants and increasing environmental concerns combined with the constant pressure to reduce operating costs and conserve energy make the development of sensors for the water industry a field ripe with opportunity. As with all areas of opportunity, to be successful in developing sensors for the water industry there are hurdles to overcome and pitfalls to be avoided. Harsh environmental conditions in wastewater sensor deployment scenarios are a given, but, the severe conditions commonly encountered in drinking water applications are often not addressed with the diligence that they merit. Factors such as debris in the pipes, turbidity, fluctuating pH, various types and levels of disinfectants and other water treatment chemicals; fluctuating redox conditions and variable conductivity are just a few of the complicating elements that can hamper sensor development. Once a sensor has been developed the last hurdle has been by no means cleared. The water industry is a notoriously reactionary group. The barriers to change are extremely large and adoption curves for new technologies tend to be extremely slow. One of the chief factors in these slow adoption rates is the glacial pace of regulatory acceptance of new methods. Even for measurements not taken for reporting purposes, the industry tends to favor methods that have EPA or other regulatory body approval. Unfortunately, this approval process can take several years (2 to 3 years for drinking water and up to 7 years for wastewater applications). There are several industry initiatives sponsored by groups, such as the Water Environment Federation, that are attempting to speed up the innovation curve in the water industry but these groups are just beginning to act and it is unknown if they will meet with success. In this presentation, the above constraints will be discussed, as well as others, which are commonly encountered in the research and development cycle for sensors tailored to the water industry. Examples of obstacles encountered in a recent development project initiated for the use of nano-fibers in sensors for the water industry will be utilized to illustrate various points. A thorough knowledge of the technological pitfalls to be avoided as well as an understanding of the market and regulatory forces that can come into play in this market arena will lead to the development of robust sensors that serve the industry and help to ensure developers of a reasonable return on investment.

Different Weights of the Evidence-Based Medicine Triad in Regulatory, Health Technology Assessment, and Clinical Decision Making.

Evidence-based medicine (EBM) is defined as a systematic approach to clinical problem solving by the integration of best research evidence with real-world clinical expertise and patient values. Since those early days, decision requirements expanded from patient-centric clinical decision making to a population-based view including regulatory health technology assessments (HTAs). Regulatory bodies mainly rely on the totality of research evidence, which includes preclinical and all available clinical data. HTA bodies primarily focus on clinical data with a strong preference for comparative data from randomized controlled clinical trials (RCTs). Conversely, bedside clinical decisions are largely driven by real-world clinical expertise, which takes into account the individual patients’ preferences, as well as the availability of supportive research evidence. While the focus on research evidence is a typical feature of the early part of the adoption curve for innovative technologies, HTA decision makers need to ensure that clinical expertise is also appropriately included in their decisions, in order to avoid beneficial medications from being not available to patients.

Diffusion of Innovation and the Technology Adoption Curve: Where Are We? The Zimbabwean Experience

The introduction of the Internet has led to the development of new technologies and applications that have been adopted by organisations and their users to enable them to survive in this age of technology. Online technologies have penetrated Zimbabwe, particularly in the retail industry, however, little research has been done to link the research results to theory particularly the technology adoption theories. Therefore the objective of this study was to explore the current extent of adoption of online transaction platforms in the retail industry in Zimbabwe. Furthermore, this paper purposed to determine the extent of this adoption on the Technology Adoption Curve. The online transaction platforms studied were Internet banking, Automated Teller Machines, Mobile banking, Point of Sale and Mobile money. The study took a mixed method approach where both qualitative and quantitative strategies were used. A three-sample dataset comprising of 268 bank and supermarket customers, 56 bank managers and 31 supermarket managers was used. The resultant adoption rates were plotted on Moore’s Technology Adoption Curve. The results indicated that although the retail industry had made great strides to adopt online technology platforms, but the adoption by customers was relatively low at a market penetration of 14.5%. The study thus concluded that Zimbabwe’s online technologies were in their infancy stage on the maturity model adoption curve and were inside Moore’s chasm on the technology adoption curve. The study recommended to customers to use the adopted online technologies in order to keep abreast with the digital era. On the other hand, banks and supermarkets were urged to devise marketing strategies in an effort to cross the chasm in the Technology Adoption Curve.

The adoption of Big Data Services by Manufacturing firms: An empirical investigation in India

Although some leading companies are actively adopting Big data services (BDS) to strengthen market competition , many manufacturing firms are still in the early stage of the adoption curve due to lack of understanding of and experience with BDS. Hence, it is interesting and timely to understand issues relevant to BDS adoption. The empirical investigation reveals that a firm’s intention to adopt BDS can be positively affected by the quality and benefits of BDS. Surprisingly, a firm’s absorptive capacity in utilizing big data and risks and costs associated with implementation and maintenance does not impact the adoption intention of BDS.

Virtual Reality: The Next Disruptor: A new kind of worldwide communication

Virtual reality (vr) will be a profoundly disruptive technology in the same way as the smartphone and the Internet. Like the smartphone, VR uses a new interface format (a head-mounted display and hand controllers) to provide much more intuitive and natural access to a computing device. Much like the Internet, it allows a new kind of worldwide communication-but this time with a natural human experience that will be nearly indistinguishable from standing face to face or in a group. The combination of these two capabilities, along with a price point similar to the first smartphones, suggests that we will see an adoption curve similar to that of the smartphone, with a majority of worldwide Internet users making daily use of VR in the next seven to ten years.

VP216 Health Technology Assessment's Balance Between Additional Data, Adoption, And Patient Access

INTRODUCTION:Historically, many Health Technology Assessment (HTA) bodies were developed with a focus on addressing rapidly rising drug costs and the unique need to evaluate each drug as a de novo entity. The degree to which the unique needs for evaluating technologies vis a vis drugs are reflected in distinct HTA methods and activity is to date understudied.METHODS:We examined HTA's reviews of two technologies: WATCHMAN™, a device to reduce the risk of stroke in certain patients and Alair™, a procedure-based treatment for severe asthma. Both technologies have been extensively reviewed by HTA bodies and payers in many countries. These HTA reviews are compared to a convenience sample of these HTA's bodies reviews of drugs and qualitative differentiators between these two categories explored.RESULTS:The differences and similarities (for example, in rigor and necessity of evidence) between US Section 510(k) clearances, US premarket approval (PMA), and US new drug application (NDA) regulatory pathways have not been clearly understood by HTA or reflected in their methodologies employed. Additionally, emergent methodologies such as Bayesian statistical analyses may encounter challenges within technologies reviews. HTA bodies may not be cognizant of development timelines or the timelines of comparators. Finally, HTA bodies may overestimate device adoption rates.CONCLUSIONS:The differences in evidence requirements for regulatory approval between US 510(k), US PMA, and US NDA pathways have not been reflected in different methodological approaches within HTA bodies reviews. Opportunities and novel methods are needed for HTA bodies to derive imputed comparisons between technologies that may have inherently incongruent timelines. Finally, HTA bodies could benefit from methods to more accurately estimate projected adoption curves. Challenges exist using frameworks, paradigms, and methodologies initially established for, and commonly used for, pharmaceuticals on device evaluations; leaders of HTA methods can improve the situation by providing guidance and recommendations for more appropriate HTA methods to evaluate devices.

The Matthew Effect in the Italian Digital Context: The Progressive Marginalisation of the “Poor”

The Matthew effect describes a model according to which, over time, inequalities fuel ever-widening gaps among individuals and social groups on the basis of the wellknown adage: “the rich get richer and the poor get poorer”. In this paper, we analyse the results of the Matthew effect in Italy in relation to first and second level digital divide, in order to determine the trajectories of closure, persistence or reinforcement of inequalities within the population. The central research question of the work aims to understand whether, when compared with a higher level of dissemination of technology over time, the adoption curves trace a model of progressive inclusion for the “poor” which approach the “richest”, or whether progressive increases are recorded in gaps. Considering a time span of more than a decade, microdata from the Istat multipurpose “Aspects of daily life” survey were used to find an empirically grounded answer to this research question. In terms of methodology, indices of absolute and relative digital exclusion and marginalisation which are necessary to take into account the changing nature of the phenomenon were proposed and used. Techniques of multivariate analysis (cluster analysis and multiple factor analysis) were also applied to detect any changes in the structure of variables and trajectories of the socio-demographic characteristics in question. The main results show the existence of a relative Matthew effect in Italy: despite the general increase in the spread of technologies, we are witnessing a progressive impoverishment of the weakest sectors of the population.

A Social Media mHealth Solution to Address the Needs of Dengue Prevention and Management in Sri Lanka.

BackgroundSri Lanka has witnessed a series of dengue epidemics over the past five years, with the western province, home to the political capital of Colombo, bearing more than half of the dengue burden. Existing dengue monitoring prevention programs are exhausted as public health inspectors (PHIs) cope with increasing workloads and paper-based modes of surveillance and education, characterizing a reactive system unable to cope with the enormity of the problem. On the other hand, the unprecedented proliferation and affordability of mobile phones since 2009 and a supportive political climate have thus far remained unexploited for the use of mobile-based interventions for dengue management.ObjectiveTo conduct a needs assessment of PHIs in Colombo with respect to their dengue-related tasks and develop a new mobile-based system to address these needs while strengthening existing systems.MethodsOne-on-one in-depth interviews were conducted with 29 PHIs to a) gain a nuanced, in-depth understanding of the current state of surveillance practices, b) understand the logistical, technological and social challenges they confront, and c) identify opportunities for mobile-based interventions. Quantitative analysis included simple descriptive statistics while qualitative analysis comprised textual analysis of 209 pages of transcripts (or nearly 600 minutes of conversations) using grounded theory approaches.ResultsCurrent paper-based data collection practices for dengue surveillance involved a circuitous, time consuming process that could take between 7-10 days to officially report and record a single case. PHIs confronted challenges in terms of unreliable, standalone GIS devices, delays in registering mosquito breeding sites and lack of engagement from communities while delivering dengue education. These findings, in concert with a high motivation to use mobile-based systems, informed the development of Mo-Buzz, a mobile-based system that integrates three components – digitized surveillance, dynamic disease mapping and digitized dengue education – on a common platform. The system was developed through an iterative, evolutionary, collaborative process, consistent with the Spiral model of software development and is currently being used by all 55 PHIs in the CMC system.ConclusionsGiven the entrenched nature of existing paper-based systems in PHIs’ work habits, we expect a gradual adoption curve for Mo-Buzz in the future. Equally, we expect variable adoption of the system with respect to its specific components, and specific PHI sub-groups (younger versus older). The Mo-Buzz intervention is a response to multiple calls by the global mHealth community for collaborations in the area of mobile interventions for global health. Our experience revealed that the benefits of this paradigm lies in alleviating country-specific public health challenges through a commonly shared understanding of cultural mores, and sharing of knowledge and technologies. We call upon future researchers to further dissect the applicability of the Spiral Model of software development to mHealth interventions and contribute to the mHealth evidence debate from theoretical and applied perspectives.

Innovation diffusion equations on correlated scale-free networks

We introduce a heterogeneous network structure into the Bass diffusion model, in order to study the diffusion times of innovation or information in networks with a scale-free structure, typical of regions where diffusion is sensitive to geographic and logistic influences (like for instance Alpine regions). We consider both the diffusion peak times of the total population and of the link classes. In the familiar trickle-down processes the adoption curve of the hubs is found to anticipate the total adoption in a predictable way. In a major departure from the standard model, we model a trickle-up process by introducing heterogeneous publicity coefficients (which can also be negative for the hubs, thus turning them into stiflers) and a stochastic term which represents the erratic generation of innovation at the periphery of the network. The results confirm the robustness of the Bass model and expand considerably its range of applicability.