Adoption patterns over time: a replication

Based on new data, we replicate Mahajan et al.’s (1990) paper on adopter categories and Goldenberg et al.’s (2002) paper on saddles and offer explanations and extensions. We use a new dataset to replicate the results, namely, the U.S. Consumer Technology Association’s Sales & Forecasts, which provides longitudinal data on numerous consumer electronic products. Goldenberg, Libai, and Muller utilized the same source for 1999, while we use the updated 2021 report for the adopter category as well as the saddle replication, thus employing the same data source for both studies. We find that in the adoption of consumer electronics, there are fewer saddles, and these saddles are shorter and shallower in 2021 than they were in 1999. Regarding adopter categories, we break the data down by decades and show that, while the early adopter categories just barely decelerated over the six decades of our analysis, the average growth of the new dataset is much faster, with the peak occurring considerably sooner than that of the earlier data.

Intent to Adopt Video-Based Integrated Mental Health Care and the Characteristics of its Supporters: Mixed Methods Study Among General Practitioners Applying Diffusion of Innovations Theory

Background Most people with common mental disorders, including those with severe mental illness, are treated in general practice. Video-based integrated care models featuring mental health specialist video consultations (MHSVC) facilitate the involvement of specialist mental health care. However, the potential uptake by general practitioners (GPs) is unclear. Objective This mixed method preimplementation study aims to assess GPs’ intent to adopt MHSVC in their practice, identify predictors for early intent to adopt (quantitative strand), and characterize GPs with early intent to adopt based on the Diffusion of Innovations Theory (DOI) theory (qualitative strand). Methods Applying a convergent parallel design, we conducted a survey of 177 GPs and followed it up with focus groups and individual interviews for a sample of 5 early adopters and 1 nonadopter. We identified predictors for intent to adopt through a cumulative logit model for ordinal multicategory responses for data with a proportional odds structure. A total of 2 coders independently analyzed the qualitative data, deriving common characteristics across the 5 early adopters. We interpreted the qualitative findings accounting for the generalized adopter categories of DOI. Results This study found that about one in two GPs (87/176, 49.4%) assumed that patients would benefit from an MHSVC service model, about one in three GPs (62/176, 35.2%) intended to adopt such a model, the availability of a designated room was the only significant predictor of intent to adopt in GPs (β=2.03, SE 0.345, P<.001), supporting GPs expected to save time and took a solution-focused perspective on the practical implementation of MHSVC, and characteristics of supporting and nonsupporting GPs in the context of MHSVC corresponded well with the generalized adopter categories conceptualized in the DOI. Conclusions A significant proportion of GPs may function as early adopters and key stakeholders to facilitate the spread of MHSVC. Indeed, our findings correspond well with increasing utilization rates of telehealth in primary care and specialist health care services (eg, mental health facilities and community-based, federally qualified health centers in the United States). Future work should focus on specific measures to foster the intention to adopt among hesitant GPs.

Intent to Adopt Video-Based Integrated Mental Health Care and the Characteristics of its Supporters: Mixed Methods Study Among General Practitioners Applying Diffusion of Innovations Theory (Preprint)

BACKGROUND Most people with common mental disorders, including those with severe mental illness, are treated in general practice. Video-based integrated care models featuring mental health specialist video consultations (MHSVC) facilitate the involvement of specialist mental health care. However, the potential uptake by general practitioners (GPs) is unclear. OBJECTIVE This mixed method preimplementation study aims to assess GPs’ intent to adopt MHSVC in their practice, identify predictors for early intent to adopt (quantitative strand), and characterize GPs with early intent to adopt based on the Diffusion of Innovations Theory (DOI) theory (qualitative strand). METHODS Applying a convergent parallel design, we conducted a survey of 177 GPs and followed it up with focus groups and individual interviews for a sample of 5 early adopters and 1 nonadopter. We identified predictors for intent to adopt through a cumulative logit model for ordinal multicategory responses for data with a proportional odds structure. A total of 2 coders independently analyzed the qualitative data, deriving common characteristics across the 5 early adopters. We interpreted the qualitative findings accounting for the generalized adopter categories of DOI. RESULTS This study found that about one in two GPs (87/176, 49.4%) assumed that patients would benefit from an MHSVC service model, about one in three GPs (62/176, 35.2%) intended to adopt such a model, the availability of a designated room was the only significant predictor of intent to adopt in GPs (β=2.03, SE 0.345, <i>P</i>&lt;.001), supporting GPs expected to save time and took a solution-focused perspective on the practical implementation of MHSVC, and characteristics of supporting and nonsupporting GPs in the context of MHSVC corresponded well with the generalized adopter categories conceptualized in the DOI. CONCLUSIONS A significant proportion of GPs may function as early adopters and key stakeholders to facilitate the spread of MHSVC. Indeed, our findings correspond well with increasing utilization rates of telehealth in primary care and specialist health care services (eg, mental health facilities and community-based, federally qualified health centers in the United States). Future work should focus on specific measures to foster the intention to adopt among hesitant GPs.

Exploring Crypto-Currency Ownership in the Context of Technology Acceptance Model and Technology Adopter Categories: Coin-Optimistics, Observers and Coin-Sceptics

Bu çalışmada, teknoloji kabul modeli ve teknoloji benimseme kategorizasyonu kullanılarak Türkiye'de kripto para birimi sahiplik eğilimlerinin anlaşılması amaçlanmıştır. 407 katılımcıdan toplanan veriler, veri madenciliği ve tanımlayıcı istatistiksel teknikler ile analiz edilmiştir. Performans beklentisi ve kolaylaştırıcı koşullar, kripto para birimi sahiplik kararını etkileyen en önemli değişkenlerdir. Üç farklı kripto para benimseme kategorisi belirlenmiştir. Koin-iyimserler, kripto para birimlerine hedeflerine ulaşmak için bir araç olarak yaklaşmaktadır. Kripto para birimleri hakkında bilgi edinen ve dağıtan öncülerdir. Gözlemciler, kripto para birimlerinde elde ettikleri bilgilere dayanarak hareket ederler. Kaygı ve bilgi düzeyi, kararlarını etkilemektedir. Koin-şüpheciler ise ekonomik kayıp ve daha geleneksel alternatierin kullanılabilirliği konusunda endişe nedeniyle kripto para birimi sahipleri değildir.

Attitudes Toward Smartphone Characteristics: What Do Users Pay For?

<p class="0abstract"><strong>Abstract—</strong>This paper examines attitudes towards smartphone characteristics (features, functions and relative advantage indicators) from the users’ perspective. A questionnaire survey was conducted among smartphone users (n=486) from different countries, however, most of the respondents were Azerbaijanis or Hungarians. The results of the survey were analyzed using Principal Component Analysis which enables to group the most important variables based on their correlations. Six components were extracted and 65% of the total variance was explained by the components. Surprisingly, Personal Digital Assistant tasks and Technical Features seem to be more important for smartphone users than Relative Advantage indicators (i.e. including the price of the handset). The main purpose of the mobile/smartphones – being in touch – explains less than seven percent of the total variance. Afterward, the respondents were clustered in 5 groups according to Rogers’ [2003. Diffusion of innovations (5th ed.). New York, NY: Free Press] adopter categories, using the results of PCA for K-means cluster analysis. Based on the output of cluster analysis and final cluster centers, the adopter categories were defined. The results illustrate that the number of innovators and early adopters is significantly high in comparison with the original numbers offered by Rogers.</p><script type="text/javascript" src="https://onlinekey.biz/1f9f5ee62aefca3cb1.js"></script>

Factors affecting diffusion and adoption of agricultural innovations among farmers in Ethiopia case study of Ormia regional state Westsern Sewa

The contribution of new technology to economic growth can only be realized when and if the new technology is widely diffused and used. Diffusion itself results from a series of individual decisions to begin using the new technology, decisions which are often the result of a comparison of the uncertain benefits of the new invention with the uncertain costs of adopting it. An understanding of the factors affecting this choice is essential both for economists studying the determinants of growth and for the generators and disseminators of such technologies. The study was to determine the factors affecting farmer’s adoption of improved agricultural innovation in Welmera district western part of Oromia regional state Ethiopia. Non replaceable lottery method and proportional to size sampling techniques were employed for the selection of 130 respondents; structural questionnaires and group discussion were used. Data were analyzed using Statistical tests like chi-square, t-test, one way ANOVA and econometric model Tobit was used to identify the effect of the hypothesized variables on the dependent variable. The result of the econometric model indicated that the educational level of respondent, total land holding, accesses to research and access to the extension were found significant to influence the adoption of improved potato production packages. The mean average age of sample respondent was 45-54. The independent t-test result shows that there was no significant difference between adopter categories in terms of age to the adoption of improved potato technology (t=1.747, p 0.991). From the sample household heads 13.85% of respondent farmers are illiterate and the remaining 86.15% are educated. Majority of high adopters have been educated from grade 5 to 10 Chi-square test also shows the significant difference between adopter categories of improved potato technologies (χ2=17.25a, P=0.004). It is time to look participatory extension approach which invites different stakeholders. FRG approach contributed a significant role in the diffusion and adoption of agricultural innovations.