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The purpose of this paper is to describe and discuss the variables which have contributed to the adoption of positive behaviour support (PBS) and applied behaviour analysis (ABA). Differences and similarities are highlighted, applications to contemporary issues in the UK and Australia are emphasised, and considerations posed for their improved adoption.

A conceptual framework for diffusion of innovations theory is used to guide the analysis of three sets of articles, and application of the framework is guided by narrative analysis.

Eight variables from the conceptual framework were identified, and the communication networks for PBS and ABA are argued to be distinct. There has been a positive change in the perception of PBS by the ABA field, but PBS has leveraged diffusion more successfully. ABA appears to have been separated from PBS in the UK while Australia is yet to fully benefit from the contributions of ABA. Those working in the fields of PBS and ABA should further collaborate for their mutual benefit.

Greater attention to the factors which promote diffusion can assist PBS and ABA to improve their adoption.

This is the first paper to use diffusion of innovations theory to analyse the adoption of PBS and ABA.

The purpose of this paper is to investigate the post-adoption stage of Voice over Internet Protocol (VoIP) telephony diffusion, examining usage behavior based on Shih and Venkatesh’s use-diffusion (UD) model.

The research model incorporates technology sophistication, complementary technologies, personal innovativeness, self-efficacy, trust propensity, media exposure, subjective norms, and word-of-mouth (WOM) referrals as UD determinants; rate of use and variety of use as usage variables; intense use, specialized use, nonspecialized use, and limited use as UD patterns; and satisfaction and intention to use future-related technologies as UD outcomes. Data used to test the research model were collected using a web-based online questionnaire form; 360 valid responses were obtained. Partial least squares, multinomial logistic regression, and analysis of variance were used to analyze data.

The results reveal that variety of use, self-efficacy, propensity to trust, media exposure, subjective norms, and WOM referrals increase rate of use, while complementary technologies, personal innovativeness, self-efficacy, media exposure, and subjective norms widen variety of use; variety of use is essential in predicting UD outcomes; when choosing limited use as the reference category, more than half of the UD determinants are capable of predicting UD patterns; and generally, intense users are more satisfied with VoIP telephony, while limited users have less intention to use future-related technologies.

The present study focuses on the post-adoption stage, thereby extending the frontiers of research on the diffusion of VoIP telephony. Academics can obtain some evidence of the explanatory power of the UD model in the context of VoIP telephony use, and practitioners can obtain fresh insights into the dynamics of VoIP telephony usage behavior.

Interactions between stainless steel and silver and gold based thick film conductors and YBa2Cu3O7 thick film superconductors were studied by SEM and EDX. The stainless steel reacted with the superconducting layer, forming a Ba‐Fe‐O compound on the interface, while BaO‐depleted YBa2Cu3O7 compound transformed into insulating Y2BaCuO5 compound. When YBa2Cu3O7 is fired on a gold thick film conductor, a thin layer of Y2BaCuO5 compound forms between the conductor and the superconducting film. In the case of a thin Ag conductor, BaO diffused through the silver film and reacted with alumina ceramic to the depth of a few μm. When a thicker silver layer (40–50 μm) was used, diffusion did not occur.

The purpose of this paper is to provide a commentary to Hayward, Poed and McKay-Brown’s article by expanding upon some of the reasons for the apparent dominance of Positive Behaviour Support (PBS).

The commentary notes the rise and dominance of Cognitive behaviour therapy (CBT) over a similar time frame, and uses both this and ideas from the literature to examine the reasons for, advantages and consequences of PBS’s dominance.

The paper identifies three overlapping reasons for the comparable rise of PBS and CBT, and examines the impact that their dominance has had on attempts to manage quality, improve outcomes and train staff.

Establishing the reasons for the success of a theoretical approach can assist with the development of other effective interventions.

This paper aims to examine the determinants that influence the customer’s adoption towards the use of family takaful scheme by extending diffusion theory of innovation (DOI) in the context of Pakistan.

The published work allied to takaful studies and DOI was reviewed. Total of 282 respondents who are non-users of family takaful product were used for the purpose of primary data collection through convenience sampling.

The customer’s adoption towards Islamic insurance is determined not only by perceived relative advantage and perceived compatibility but also by awareness and religious belief. Perceived complexity, on the contrary, turns out not to be a predictor of family takaful adoption. Further, gender, age and education do not moderate the family takaful adoption by the customers.

This research alike others have limitations in terms of sampling method used and only covers one city of Pakistan, namely, Karachi. Further studies need to be conducted in other cities as well with a large population.

Extended DOI is not used in the context of takaful in Pakistan as evident from scarce literature on the empirical studies. Therefore, the authors extend the DOI in the current work. Further, this paper will be a useful reference guide for the academicians, operators of takaful business and future researchers.

We conduct a detailed simulation study of the forecasting performance of diffusion index-based methods in short samples with structural change. We consider several data generation processes, to mimic different types of structural change, and compare the relative forecasting performance of factor models and more traditional time series methods. We find that changes in the loading structure of the factors into the variables of interest are extremely important in determining the performance of factor models. We complement the analysis with an empirical evaluation of forecasts for the key macroeconomic variables of the Euro area and Slovenia, for which relatively short samples are officially available and structural changes are likely. The results are coherent with the findings of the simulation exercise and confirm the relatively good performance of factor-based forecasts in short samples with structural change.

The purpose of this paper is to identify and empirically examine antecedents to environmentally conscious operations (ECOs) and to investigate the effect of ECOs on performance in the transitioning economy Russia. The authors examine organizational antecedents such as innovativeness, proximity to major urban centers of gravity, and interaction with international customers because there is evidence that external antecedents such as stakeholder pressure may not be as effective in transitioning economies as they are in developed countries.

The authors develop a structural equation model to test the hypothesized relationships on a sample of 769 private sector manufacturing companies located in Russia having used the survey method for data collection.

The authors find that the proximity to major urban centers of gravity, interaction with international customers and a firm-internal culture of innovativeness are positively related to ECOs. The authors also find evidence that ECOs are positively related to performance. ECOs partially mediate the relationship between international trade and firm performance.

This is one of the first studies to investigate antecedents to ECOs in Russia and one of the few that uses diffusion of innovations theory to unpack potential ECO antecedents.

This paper aims to focus on the diffusion process of Knowledge management systems (KMSs). Specifically, to identify the sequence of stages of the process.

The paper presents a six‐stage model of the KMS diffusion process. It then provides an empirical test of the sequence of steps in the KMS diffusion process in Australia. Structural equation modelling using LISREL is used as the analytical tool for the empirical test.

The results show that all the hypotheses related to the sequence of the KMS diffusion process are significant. This is an important and significant finding. It clearly demonstrates how KMS adoption and diffusion should be planned in Australian organizations.

The research identifies six stages of KMS diffusion based on the results from selected Australian companies: initiation, adoption, pilot implementation, organic growth, organizational implementation, and sustained use/diffusion of KMS.

We address the problem of selecting the common factors that are relevant for forecasting macroeconomic variables. In economic forecasting using diffusion indexes, the factors are ordered, according to their importance, in terms of relative variability, and are the same for each variable to predict, that is, the process of selecting the factors is not supervised by the predictand. We propose a simple and operational supervised method, based on selecting the factors on the basis of their significance in the regression of the predictand on the predictors. Given a potentially large number of predictors, we consider linear transformations obtained by principal components analysis. The orthogonality of the components implies that the standard t-statistics for the inclusion of a particular component are independent, and thus applying a selection procedure that takes into account the multiplicity of the hypotheses tests is both correct and computationally feasible. We focus on three main multiple testing procedures: Holm's sequential method, controlling the familywise error rate, the Benjamini–Hochberg method, controlling the false discovery rate, and a procedure for incorporating prior information on the ordering of the components, based on weighting the p-values according to the eigenvalues associated to the components. We compare the empirical performances of these methods with the classical diffusion index (DI) approach proposed by Stock and Watson, conducting a pseudo-real-time forecasting exercise, assessing the predictions of eight macroeconomic variables using factors extracted from an U.S. dataset consisting of 121 quarterly time series. The overall conclusion is that nature is tricky, but essentially benign: the information that is relevant for prediction is effectively condensed by the first few factors. However, variable selection, leading to exclude some of the low-order principal components, can lead to a sizable improvement in forecasting in specific cases. Only in one instance, real personal income, we were able to detect a significant contribution from high-order components.

The aim of this study is to examine the trend over time of the demand for .it domain names.This study first assesses whether there is a phase of growth and expansion or at a point of saturation. Second, this research can be useful also to compare researches that have considered other internet metrics and other models.

This paper describes the forecasting methods used to analyze the internet diffusion in Italy. The domain names under the country code top-level domain “.it” have used as metrics. To predict domain names .it the seasonal auto regressive integrated moving average (SARIMA) model and the Holt-Winters (H-W) methods have been used.

The results show that, to predict domain names .it the SARIMA model is better than the H-W methods. According to the findings, notwithstanding the forecast of a growth in domain names, the increase is however limited (about 3%), tending to reach a phase of saturation of the market of domain names .it.

In general many authors have studied internet diffusion applying statistical models that follow an S-shaped behavior. On the other hand, the more used diffusion models that follow an S-shape not always provide an adequate description of the Internet growth pattern. To achieve this goal, this paper demonstrates how the time series models, in particular SARIMA model and H-W models, fit well in explaining the spread of the internet.