Early Adopters
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2021 ◽  
Vol 21 (1) ◽  
Genevieve N. Healy ◽  
Elisabeth A. H. Winkler ◽  
Ana D. Goode

Abstract Background The web-based BeUpstanding program supports desk workers to sit less and move more. Successfully translated from a research-delivered intervention, BeUpstanding has gone through iterative development and evaluation phases in preparation for wide-scale implementation. In the third planned “early-adopters” phase (01/09/2017–11/06/2019), the program was made freely-available online. An integrated delivery and evaluation platform was also developed to enable workplace champions to run and evaluate the intervention within their work team independent of researcher support. Using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework, this study reports on the extent to which the program and processes were “fit-for-purpose” for a national implementation trial across the indicators of uptake (reach and adoption), implementation and engagement, and effectiveness for behaviour change. Methods Data were collected via the online surveys embedded in the program and through program access analytics. Descriptive data (with linearized variance for the clustered staff-level data) and results from mixed models (repeated data and clustering for pre-post changes) are reported. Results Despite purposeful limited promotion, uptake was good, with 182 Australian users initially registering (208 total) and 135 (from 113 organisations) then completing the sign-up process. Recruitment reached users across Australia and in 16 of 19 Australian industries. Implementation was inconsistent and limited, with signed-up users completing 0 to 14 of the program’s 14 steps and only 7 (5.2%) completing all seven core steps. Many champions (n = 69, 51.1%) had low engagement (1 day toolkit usage) and few (n = 30, 22%) were highly engaged (> 1 day toolkit usage and surveyed staff). Although only 18 users (7 organisations) performed the pre- and post-program staff evaluations (337 and 167 staff, respectively), pre-post changes showed the program effectively reduced workplace sitting by − 9.0% (95% CI -12.0, − 5.9%). Discussion The program had uptake across industries and across Australia, but implementation and engagement varied widely. Few workplaces completed the evaluation components. In those that did, the program was effective for the primary outcome (workplace sitting). Conducting a planned early adopters phase and a comprehensive evaluation according to RE-AIM helped highlight necessary program improvements to make it more suitable for wide-scale implementation and evaluation. Trial registration Australian and New Zealand Clinic Trials Registry ACTRN12617000682347. Date registered: 12/05/2017.

2021 ◽  
Vol 27 (1) ◽  
Hideaki Hata ◽  
Nicole Novielli ◽  
Sebastian Baltes ◽  
Raula Gaikovina Kula ◽  
Christoph Treude

AbstractDiscussions is a new feature of GitHub for asking questions or discussing topics outside of specific Issues or Pull Requests. Before being available to all projects in December 2020, it had been tested on selected open source software projects. To understand how developers use this novel feature, how they perceive it, and how it impacts the development processes, we conducted a mixed-methods study based on early adopters of GitHub discussions from January until July 2020. We found that: (1) errors, unexpected behavior, and code reviews are prevalent discussion categories; (2) there is a positive relationship between project member involvement and discussion frequency; (3) developers consider GitHub Discussions useful but face the problem of topic duplication between Discussions and Issues; (4) Discussions play a crucial role in advancing the development of projects; and (5) positive sentiment in Discussions is more frequent than in Stack Overflow posts. Our findings are a first step towards data-informed guidance for using GitHub Discussions, opening up avenues for future work on this novel communication channel.

2021 ◽  
Eli Asikin-Garmager ◽  
Amir E. Aharoni ◽  
Pau Giner ◽  
Nik Gkountas

Content Translation has been used since 2015 by more than 78,000 editors to create over 800,000 Wikipedia articles. Section Translation aims to bring the option of translation to mobile editors, and can be used to expand existing articles. The session will present a brief history of the research and design process that led to Section Translation, as well as recent research done on the experiences of early adopters. It will conclude with a short demo of Section Translation.

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Brian Leavy

Purpose The Fail-Safe Startup: Your Roadmap for Entrepreneurial Success, the new book by entrepreneurship researcher Tom Eisenmann, sets out to help improve the odds by looking more closely at the most prevalent causes of startup failure and how to avoid them. Design/methodology/approach Eisenmann research led him to identify six distinct patterns that explain a large proportion of startup failures, three relating to early stage failures and three to late stage. Findings Strong demand from early adopters may lead a founder to scale up prematurely. Practical/implications Entrepreneurs must research differences in the needs of likely early adopters and mainstream customers during the upfront customer discovery phase. Originality/value Entrepreneurs must research differences in the needs of likely early adopters and mainstream customers during the upfront customer discovery phase. 10; 10;The line between visionary entrepreneur and cult leader can become blurry, and a founder?s ?reality distortion field--useful for motivating others to help pursue the founder?s dream?can become a liability.

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Jiju Antony ◽  
Michael Sony ◽  
Olivia McDermott ◽  
Sandy Furterer ◽  
Matthew Pepper

PurposeIndustry 4.0 is a new trend among organizations. Some organizations have been early adopters or later adopters of Industry 4.0. The purpose of this paper is to investigate how performance effects vary between early and late adopters of Industry 4.0.Design/methodology/approachThis study applies a qualitative research methodology using grounded theory. 14 senior management professionals who have implemented Industry 4.0 participated in this study through a theoretical and snowball sampling approach. These professionals were from manufacturing and service sectors, from North America, Europe and Asia. The study used semi structured open-ended interviews to capture the organizational performance on operational, financial, environmental and social dimensions.FindingsThe findings were analyzed in terms of four broad themes which emerged from the interviews. In operational performance the operational and implementation cost will be higher for early adopters. The late adopters may enjoy the advantage in terms of improved business models. In terms of financial performance, the early adopters may see a marginal increase in profit and increased stock price compared to late adopters. The performance on the environmental dimension will see early adopters enjoying material efficiency, energy savings and an improved image of the company compared to late adopters. In social performance, the early adopters will provide a better quality of work life, safer manufacturing environment. However, the resistance from labor unions will be higher for early adopters compared to late adopters.Practical implicationsOrganizations must decide the timing of implementation of Industry 4.0. This study will act as a guide wherein they can decide to be an early adopter or late adopter based on knowledge of the resulting performance consequences.Originality/valueThis is the first paper that studies the performance effects of early versus late adopters of Industry 4.0.

2021 ◽  
Vol 5 ◽  
Keri Szejda ◽  
Moritz Stumpe ◽  
Ludwig Raal ◽  
Claire E. Tapscott

The purpose of this study was to assess the likelihood of consumer adoption of plant-based and cultivated meat in South Africa as a pathway to a healthy, sustainable, and equitable food supply. We recruited a large sample of South Africans representative across age (18–61), gender, race, and income to participate in an online survey. Participants responded to a range of measures including adoption indicators, estimated yearly intake, motivators for purchasing, desired product characteristics, preferred species, and sociodemographics. We found a high degree of openness to both products. For plant-based meat, 67% were highly likely to try and 59% were highly likely to purchase. For cultivated meat, 60% were highly likely to try and 53% were highly likely to purchase. The highest acceptance was amongst the younger generations: 60% of born-frees, 62% of millennials, and 53% of Gen X were highly likely to purchase plant-based meat and 55% of born-frees, 55% of millennials, and 46% of Gen X were highly likely to purchase cultivated meat. For the general population, we observed that future meat intake was estimated to be split equally among the three meat categories (conventional, cultivated, and plant-based). We found early adopters (those highly likely to purchase) to be quite similar in attitudinal and sociodemographic characteristics in comparison to the general population. The study findings suggest that both plant-based and cultivated meat could be viable market-based options for improving the food system in South Africa, as consumers across all segments of society, and especially amongst the younger population, indicated broad acceptance.

Ryland Corchis-Scott ◽  
Qiudi Geng ◽  
Rajesh Seth ◽  
Rajan Ray ◽  
Mohsan Beg ◽  

Among early adopters of wastewater monitoring for SARS-CoV-2 have been colleges and universities throughout North America, many of whom are using this approach to monitor congregate living facilities for early evidence of COVID-19 infection as an integral component of campus screening programs. Yet, while there have been numerous examples where wastewater monitoring on a university campus has detected evidence for infection among community members, there are few examples where this monitoring triggered a public health response that may have averted an actual outbreak.

2021 ◽  
Vol 135 (20) ◽  
pp. 2357-2376
Wei Yan Ng ◽  
Shihao Zhang ◽  
Zhaoran Wang ◽  
Charles Jit Teng Ong ◽  
Dinesh V. Gunasekeran ◽  

Abstract Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garnered significant attention due to the availability of large amounts of data and digitized ocular images. Currently, AI in Ophthalmology is mainly focused on improving disease classification and supporting decision-making when treating ophthalmic diseases such as diabetic retinopathy, age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity (ROP). However, most of the DL systems (DLSs) developed thus far remain in the research stage and only a handful are able to achieve clinical translation. This phenomenon is due to a combination of factors including concerns over security and privacy, poor generalizability, trust and explainability issues, unfavorable end-user perceptions and uncertain economic value. Overcoming this challenge would require a combination approach. Firstly, emerging techniques such as federated learning (FL), generative adversarial networks (GANs), autonomous AI and blockchain will be playing an increasingly critical role to enhance privacy, collaboration and DLS performance. Next, compliance to reporting and regulatory guidelines, such as CONSORT-AI and STARD-AI, will be required to in order to improve transparency, minimize abuse and ensure reproducibility. Thirdly, frameworks will be required to obtain patient consent, perform ethical assessment and evaluate end-user perception. Lastly, proper health economic assessment (HEA) must be performed to provide financial visibility during the early phases of DLS development. This is necessary to manage resources prudently and guide the development of DLS.

2021 ◽  
pp. 154596832110462
Meg E. Morris ◽  
Susan C. Slade ◽  
Joanne E. Wittwer ◽  
Irene Blackberry ◽  
Simon Haines ◽  

Background Therapeutic dancing can be beneficial for people living with Parkinson’s disease (PD), yet community-based classes can be difficult to access. Objective To evaluate the feasibility and impact of online therapeutic dancing classes for people in the early to mid-stages of PD. Methods Co-produced with people living with PD, physiotherapists, dance teachers and the local PD association, the ‘ParkinDANCE’ program was adapted to enable online delivery during the COVID-19 pandemic. Participants completed 8 one-hour sessions of online therapeutic dancing. Each person was assigned their own dance teacher and together they selected music for the classes. A mixed-methods design enabled analysis of feasibility and impact. Feasibility was quantified by attendance and adverse events. Impact was determined from individual narratives pertaining to consumer experiences and engagement, analysed with qualitative methods through a phenomenological lens. Results Attendance was high, with people attending 100% sessions. There were no adverse events. Impact was illustrated by the key themes from the in-depth interviews: (i) a sense of achievement, enjoyment and mastery occurred with online dance; (ii) project co-design facilitated participant engagement; (iii) dance instructor capabilities, knowledge and skills facilitated positive outcomes; (iv) music choices were key; and (v) participants were able to quickly adapt to online delivery with support and resources. Conclusions Online dance therapy was safe, feasible and perceived to be of benefit in this sample of early adopters. During the pandemic, it was a viable form of structured physical activity. For the future, online dance may afford benefits to health, well-being and social engagement.

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