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The purpose of this paper is to forecast the performance and adoption of residential energy management technology (REMT).

The paper integrates scenarios, analogy and Bass diffusion model.

Four different scenarios were identified. Bass curve parameters were extracted through many different existing devices, and then fit into each scenario subjectively to produce four different kinds of diffusion curves.

The approach and the model provide decision and policy makers to evaluate how they should position new energy management innovations based on the desired type of diffusion.

This study aims to examine the diffusion pattern of International Financial Reporting Standards (IFRS) into the countries and identifies the channels of diffusion.

Data includes 98 countries that made a public commitment to IFRS adoption from 2000 to 2016. Adoptee countries are categorized based on Rogers’ adopter categorizations to understand country characteristics. Innovation diffusion models such as internal influence, external influence and bass diffusion that explain diffusion patterns are applied to the cumulative adoption of IFRS. Coefficients of internal and external diffusion are obtained using the models to explain the mode of diffusion pattern of IFRS. Further tests are done to identify the best model that explains IFRS diffusion.

Findings show that IFRS diffusion is a result of external influence through vertical communication from a centralized body (IASB) and internal influence due to imitation and interpersonal communication between countries. Imitation effect among the countries has a stronger effect on the IFRS adoption compared to the communication obtained from IASB.

Findings obtained can help standards-setting bodies, organizations and countries to understand the potential future of adopters and non-adopters. It facilitates the standard-setting bodies to manage IFRS diffusion by implementing measures to spread the word on the benefits of IFRS.

The study generates value by modeling a successive increase in the number of IFRS adoptee countries using empirical methods and identifies the reasons for the diffusion life cycle of IFRS.

There are many potential measures of performance for evaluating the success of a construction project. All address performance in three key areas: scope, schedule and budget. In a performance measurement framework where senior management wishes to minimise the numberof performance measures it employs, while ensuring maximal coverage or visibility into the programme, having a tool that captures each of the three areas would be ideal. Project managers have used Earned Value Management (EVM) for over 40 years to track actual schedule progress and actual costs against project plans. Earned Value Management has traditionally been applied to individual projects on which the manager is accountable for both schedule and cost variances. This paper proposes methods to apply EVM principles to allow: (1) analysis of portfolios of construction projects; (2) incorporation of the analysis into an innovative pay‐for‐performance human resources practice; and (3) use of regression analysis to develop baseline earned value curves. These extensions fit the needs of many government managers, who oversee a range of projects by multiple contractors, and whose cost risk is primarily due to schedule slips and change orders. Earned Value Management is described in the context of project oversight, and a dashboard system of performance measures is proposed for quickly assessing individual projects and portfolios. A method of generating standardised planned value curves is then specified, based on data from previous and ongoing projects. The paper concludes by showing how the US General Services Administration’s Public Buildings Service is using these methods to analyse and oversee its portfolio of new construction and major repair and alteration projects.

The purpose of this paper is to examine (1) how the recovery speed using promotional investment and (2) distributed production using additive manufacturing (AM) improve the resilience of the supply chain to manage any disruptions in the diffusion of green products.

The environmental performance, service level performance and economic performance are the measures of interest. These measures are studied through the integration of inventory and production planning (I&PP) of the reverse logistics system and consumer behavior using Bass (1969) model of diffusion of innovation under the paradigm of Industry 4.0 architecture. The Taguchi experimental design framework was used for the simulation analysis.

The adoption patterns based on the Bass model in conjunction with recovery speed and production on AM during the disruption period suggest that there exist tradeoff decisions between various combinations of information-sharing and I&PP policies.

The extensive sensitivity analyses provide real-time support for managerial decisions. Besides the potentials of Industry 4.0 capabilities, the present research suggests paying close attention to the recovery speed in conjunction with the inventory management system.

The integration of consumers' behavior (Bass model) to digital technologies is an additional contribution of the present research toward sustainability issues from the social perspective.

Previous research studies have discussed resilience to manage the ripple effect. However, none of them have addressed the changing scope of resilience to manage the ripple effect caused by the disruption in the diffusion of green products in a reverse logistics setup.

The purpose of this paper is to study how, in recent decades, manufacturing sites have reacted to changes in their business environment by developing their management practices. This also makes it possible to predict the behavior and lifecycles of the new practices adopted by companies.

The study is longitudinal, conducted in 1993, 2004 and 2010. It used a standard questionnaire, based on the “Made in Europe” benchmarking study. The results were analysed by Friedman's rank order method. The use of a three (or more) data‐point longitudinal study is the clearest way to reveal changes in, and the behaviour of, the practices.

The data suggest that there is a lifecycle for the practices used in companies and that many of the practices adopted in the late 1990s are already out of date. However, personnel‐related practices seem to last longer than process‐related practices. Furthermore, these practices seem to follow the curve of the Bass diffusion model.

The results are restricted to the sample which is quite small in size, but gives a good insight into the changes occurring within it. This research excludes new practices adopted in recent years, and uses only the questions designed for the first year in which the study was conducted. However, its purpose is to show the evolution of some practices in the form of a unique, longitudinal study. The study also indicates the lifecycles of the practices included in the sample.

This study is the only longitudinal study in its field to use data from three different time periods. It can thus look into and analyze change from a unique perspective.

The purpose of the study is applying and comparing models that predict optimal time for new product exit based on its demand pattern and survivability. This is to decide whether or not to continue investing in new product development (NPD).

The study investigates the optimal time for new product exit within the hi-tech sector by applying three models: the dynamic learning demand model (DLDM), the generalized Bass model (GBM) and the hazard model (HM). Further, for inter- and intra-model comparison, the authors conducted a simulation, considering Weiner and exponential price functions to enhance generalizability.

While higher price volatility signifies an unstable technology, greater investment into research and development (R&D) and marketing results in higher product adoption rates. Imitators have a more prominent role than innovators in determining the longevity of hi-tech products.

The study conducts a comparison of three different models considering time-varying parameters. There are four scenarios, considering variations in advertising intensity and content, word-of-mouth (WOM) effect, price volatility effect and sunk cost effect.

The purpose of this paper is to propose a model that predicts information communication technology (ICT) adoption in saturated markets, to analyze the shifting behaviors of the subscribers and to present the results.

By developing an expanded Bass model, the authors analyzed post‐adopters' switching behaviors in the mobile phone market, using the officially verified time‐series data for the number of mobile phone adopters in South Korea.

The results show that: the expanded Bass diffusion model can delineate the stream of post‐adopter's switching behaviors in saturated ICT markets; based on innovation value p and imitation value q, the authors conclude that customer immigrations during market saturated period are mainly caused by innovation effect, which is closely related to launching innovative products; and fast mover still has its imitation effect, which is closely related to first mover advantage.

The paper provides novel insights for the frame of practical strategies. To survive in a saturated market, late movers should continuously develop new and innovative products. In addition, fast movers should also develop innovative products to prevent customer immigrations, and they also should utilize their first mover advantage more efficiently, even in matured market.

The aim of this paper is to explore the benefit of forecasting emerging biomedical therapy technologies as well as the rate of diffusion of resultant biomedical products in the context of management of technology.

The research method is exploratory using a case study approach. Techniques such as bibliometric analysis and the Bass diffusion model are utilized to assess the growth rate and market penetration of pulsed electromagnetic field therapy (PEMF) as a technology.

The penetration and growth rate of user acceptance of the technology in a global context are simulated across a 15‐year period. The technology forecasting model is also used in a case study to simulate the penetration of a product using ten years' medical application data of a patented pulsed electromagnetic field for biomedical therapy application in the global context. Useful correlation between bibliometric data for PEMF and real data for the case study is illustrated.

The research is limited to the case of PEMF data presented. Further research may be done on other PEMF technology applications.

Aspects of a holistic management model that was developed for high technology companies are invoked in the practical realisation of the professional paradigm shift required when an emerging biomedical therapy technology is in the process of becoming mainstream.

It has been shown that technology diffusion traces exist for PEMF therapy technology as is evident from the bibliometric PEMF global data presented. Furthermore it is possible to simulate the PEMF therapy technology diffusion process with a Bass diffusion model incorporating innovation imitation and market size coefficients.

The purpose of this paper is to address both the evolutionary and control aspects associated with the management of artificial superintelligence. Through empirical analysis, the authors examine the diffusion pattern of those high technologies that can be considered as forerunners to the adoption of artificial superintelligence (ASI).

The evolutionary perspective is divided into three parts, based on major developments in this area, namely, robotics, automation and artificial intelligence (AI). The authors then provide several dynamic models of the possible future evolution of superintelligence. These include diffusion modeling, predator–prey models and hostility models. The problem of control in superintelligence is reviewed next, where the authors discuss Asimov’s Laws and IEEE initiative. The authors also provide an empirical analysis of the application of diffusion modeling to three technologies from the industries of manufacturing, communication and energy, which can be considered as potential precursors to the evolution of the field of ASI. The authors conclude with a case study illustrating emerging solutions in the form of long-term social experiments to address the problem of control in superintelligence.

The results from the empirical analysis of the manufacturing, communication and energy sectors suggest that the technology diffusion model fits well with the data of robotics, telecom and solar installations till date. The results suggest a gradual diffusion process, like any other high technology. Thus, there appears to be no threat of “existential catastrophe” (Bostrom, 2014). The case study indicates that any future threat can be pre-empted by some long-term social measures.

This paper contributes to the emerging stream of artificial superintelligence. As humanity comes closer to grappling with the important question of the management and control of this technology for the future, it is important that modeling efforts be made to understand the extant perspective of the development of the high-technology diffusion. Presently, there are relatively few such efforts available in the literature.

Change, Adaption and Development are central to modern marketing. This paper provides an overview of the subject area of innovation and new product development. The critical role of change in modern industrial and industrialising societies is explored in depth. Emphasis is given to the managerial implications of research to date. Key issues such as the interplay between creativity, the motivating force behind invention, and control, the basis for successful innovation, are reviewed Changing attitudes and approaches to new product development play a major part in this study, particularly the growing awareness that innovation is no longer an end in itself, but a part of a carefully controlled marketing system.