Over the hill? Exploring the other side of the Rogers' innovation diffusion model from a consumer and business model perspective

Wells, Peter Erskine; Nieuwenhuis, Paul Andre Henri Francois

doi:10.1016/j.jclepro.2018.05.144

Cited by 18 publications

(6 citation statements)

References 26 publications

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“…Creation Logic (Zott and Amit, 2012) Efficient material-technical loops (Bocken et al, 2016;Esposito et al, 2018;Geissdoerfer, Morioka, Carvalho, & Evans, 2018;Hopkinson, Zils, Hawkins, & Roper, 2018;Khan et al, 2018;Leising et al, 2018) Effective product-service loops (Hobson, Lynch, Lilley, & Smalley, 2018;Pialot, Millet, & Bisiaux, 2017;Tukker, 2015;Yang, Smart, Kumar, Jolly, & Evans, 2018) Social-collaborative loops (Lacy & Rutqvist, 2015;Todeschini, Cortimiglia, Callegaro-de-Menezes, & Ghezzi, 2017;Wells & Nieuwenhuis, 2018) Symbiotic ecosystems (Bocken et al, 2014;Gallo et al, 2018;Stal & Corvellec, 2018)…”

Section: Cbm Valuementioning

confidence: 99%

A systemic logic for circular business models

Fehrer

Wieland

2021

Journal of Business Research

136

123

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Section: Cbm Valuementioning

confidence: 99%

A systemic logic for circular business models

Fehrer

Wieland

2021

Journal of Business Research

136

123

View full text Add to dashboard Cite

“…Furthermore, Wells & Nieuwenhuis (2018) found in their research that the Rogers model approach is only half-complete because it only considers new technology adoption rather than preservation or rejection. In addition, the Rogers model includes certain "behavioral" characteristics; the decision to keep complicated and long-lasting objects has social, cultural, and emotional components that may outweigh concerns about efficiency or financial prudence.…”

Section: Innovation Diffusion Theory (Idt)mentioning

confidence: 99%

Factors that Influence AI Investment Decisions in Oman's Hydrocarbons Industry: A Review of the Theoretical Literature and Proposed Theoretical Mode

2024

qas

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Artificial Intelligence technologies have exceptional capabilities that enhance the production efficiency of oil and gas companies by saving time and energy in terms of the technologies used and the workers involved, provided that the technology used is cost-effective for the field of concern. AI gradually penetrates various stages of the oil and gas sectors, such as intelligent drilling, intelligent development, intelligent pipelines, and intelligent refineries. This paper presents a comprehensive model for studying the elements that drive AI investment decisions in Oman's hydrocarbons industry. This study combined theories of the technology acceptance model (TAM), innovation diffusion theory (IDT), business environment factors, and risk management with AI investment decisions. This study aims to discover the key factors that influence AI investment decisions by studying numerous elements that impact AI investment decisions in the sector. These drivers include AI's innovation attributes, the external and internal business environment, risk management, and the moderating of perceived usefulness and perceived ease of use. Gaining insight into these elements enables players in Oman's hydrocarbons sector to make informed decisions about AI investment decisions. This, in turn, can lead to increased operational efficiency, cost savings, and better decisionmaking processes.

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“…Its theoretical basis is the traditional writings of Helfat and Peteraf (2009); Dosi et al (2008); Winter (2003); Zollo and Winter (2002); Bygdas (2006); Eisenhardt and Martin (2000); Teece (2007); Collis (1994); McKelvie and Davidsson (2009); Pavlou and El Sawy (2011); Wang and Ahmed (2007); Zahra et al (2006); Ambrosini et al (2009); Helfat and Peteraf (2003, 2009); and Helfat and Winter (2011). For this study, the papers of the following authors are consistent with the premises of dynamic capabilities as drivers of the circular economy in innovation environments: Leising et al (2018); Rocchetti et al (2018); Horvath et al (2019); Parida and Wincent (2019); Singh et al (2019); Jakhar et al (2019); Jensen et al (2019); Albert (2019); Prieto‐Sandoval et al (2018); Watanabe et al (2019); Zucchella and Previtali (2019); Colombo et al (2019); Hankammer et al (2019); Wells and Nieuwenhuis (2018); Lesakova (2019); Horvath et al (2019); Konietzko et al (2020); Hoffmann et al (2020); Sanchez‐Levoso et al (2020); Henry et al (2019); Gong et al (2020); Scarpellini, Valero‐Gil, et al (2020); Scarpellini, Marín‐Vinuesa, et al (2020); and Mathews (2020). Proposition Relational capabilities positively influence the transition of the circular economy supported by innovation.…”

Section: Data Presentation and Analysismentioning

confidence: 99%

Circular economy and innovation: A look from the perspective of organizational capabilities

Sehnem

Queiroz

Pereira³

et al. 2021

Bus Strat Env

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Studies of circular economy and innovation have received increasing attention in academic literature in the last quadrennium. They fill a managerial gap on how to operationalise the transition from the linear model to the circular. They show practices, tools and mechanisms that allow creation of business models based on circularity premises. In this article, we seek to identify the intellectual contours of this emerging field, conducting a review of the basic conceptual framework, with an analysis of articles published on the topic of circular economy and innovation. In summary, a systematic literature review was developed, whose final bibliographic portfolio consisted of 94 analysed papers. The results of the study show that eco-innovation and innovation in business models are highlighted in this field. They are operationalised, mainly, by activating dynamic, relational and absorptive capabilities. The most important innovation practices in this context are waste management, eco-design business models, product leasing and collaborative commerce. The main contribution of this study is to pave the way for new conceptual developments in organisational capabilities to make transition sustainable and serve as a support arsenal for the maturation of circular economy studies supported by the theory of innovation and also to link management practices to operational processes in the business and production environment favouring the transition to a circular business model. It generates insights for the scientific progression of studies and shows propositions that can be validated in future quantitative studies.

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Over the hill? Exploring the other side of the Rogers' innovation diffusion model from a consumer and business model perspective

Cited by 18 publications

References 26 publications

A systemic logic for circular business models

A systemic logic for circular business models

Factors that Influence AI Investment Decisions in Oman's Hydrocarbons Industry: A Review of the Theoretical Literature and Proposed Theoretical Mode

Circular economy and innovation: A look from the perspective of organizational capabilities

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