A mixed-method analysis of Industry 4.0 technologies in value generation for collaborative consumption companies
| Authors | |
|---|---|
| Year of publication | 2023 |
| Type | Article in Periodical |
| Magazine / Source | MANAGEMENT DECISION |
| MU Faculty or unit | |
| Citation | |
| Web | https://www.emerald.com/insight/content/doi/10.1108/MD-04-2023-0618/full/html |
| Doi | http://dx.doi.org/10.1108/MD-04-2023-0618 |
| Keywords | Bayesian best-worst method; Industry 4 technologies; Collaborative consumption; Sharing economies |
| Attached files | |
| Description | This research aims to extract Industry 4.0 technological building blocks (TBBs) capable of value generation in collaborative consumption (CC) and the sharing economy (SE). Furthermore, by employing a mixed methodology, this research strives to analyse the relationship amongst TBBs and classify them based on their impact on CC.Design/methodology/approachDue to the importance of technology for the survival of collaborative consumption in the future, this study suggests a classification of the auxiliary and fundamental Industry 4.0 technologies and their current upgrades, such as the metaverse or non-fungible tokens (NFT). First, by applying a systematic literature review and thematic analysis (SLR-TA), the authors extracted the TBBs that impact on collaborative consumption and SE. Then, using the Bayesian best-worst method (BBWM), TBBs are weighted and classified using experts' opinions. Eventually, a score function is proposed to measure organisations' readiness level to adopt Industry 4.0 technologies.FindingsThe findings illustrated that virtual reality (VR) plays a vital role in CC and SE. Of the 11 TBBs identified in the CC and SE, VR was selected as the most determinant TBB and metaverse was recognised as the least important. Furthermore, digital twins, big data and VR were labelled as "fundamental", and metaverse, augmented reality (AR), and additive manufacturing were stamped as "discretional". Moreover, cyber-physical systems (CPSs) and artificial intelligence (AI) were classified as "auxiliary" technologies.Originality/valueWith an in-depth investigation, this research identifies TBBs of Industry 4.0 with the capability of value generation in CC and SE. To the authors' knowledge, this is the first research that identifies and examines the TBBs of Industry 4.0 in the CC and SE sectors and examines them. Furthermore, a novel mixed method has identified, weighted and classified pertinent technologies. The score function that measures the readiness level of each company to adopt TBBs in CC and SE is a unique contribution. |