Illustration of open codes from interview excerpts and expert IDs
| Excerpt | Interview excerpt | Expert IDs | Identified open codes |
|---|---|---|---|
| 1 | One of the main challenges in an Industry 4.0 environment is the absence of a clear strategic vision, which often results in misaligned and uncoordinated digital knowledge initiatives by organizations. Though BDA/AI generate massive volumes of data, much of it is not translated into actionable insights because of weak learning mechanisms and insufficiently skilled manpower. In addition, lack of coordination between human and Industry 4.0 technological systems results in the underutilization of employees' valuable tacit and experiential knowledge | R01, R02, R03, R04, R05, R10, R18 | Clear strategic vision; massive volume of data; weak learning mechanisms and unskilled manpower; human–AI collaboration |
| 2 | Owing to the absence of standardized semantic models and ontologies, the codification of tacit knowledge gained through experiential insights remains difficult. Also, production pressures limit the time and resources available for codifying digital and analytical insights gained from IoT devices. Insufficient integration of legacy and digital systems fragments the organizational knowledge base, while rapid technological change in the I4.0 environment quickly renders documented procedures obsolete | R04, R06, R12, R13, R17, R19 | Codification of tacit knowledge; standard semantic models and ontologies; time and resources; insufficient integration of legacy and digital systems; rapid technology change |
| 3 | Knowledge sharing within and across departments is hindered by organizational resistance and weak cross-functional collaboration. Poor knowledge transfer mechanisms across functional managers further limit knowledge exchange. In addition to interoperability constraints, limited trust, data security, and privacy concerns among employees constrain digital transfer and reduce sharing effectiveness | R01, R05, R06, R14, R15, R20 | Organizational resistance and weak cross functional collaboration; knowledge transfer mechanisms; interoperability constraints and limited trust; data security; and privacy concerns |
| 4 | Knowledge obtained from digital repositories or machine-generated analytics dashboards is often not integrated into the operational workflow. For instance, although production managers may have access to real-time machine performance data, they lack the tools or decision support systems to use it in scheduling or maintenance decisions. This underscores the need for infrastructure to effectively utilize and share generated knowledge. In addition, weak leadership support, insufficient feedback, and lack of learning loops hinder the effective utilization of AI generated knowledge across the departments | R02, R07, R08, R09, R11, R16 | KM and business processes misalignment; lack of tools or decision support systems; machine-generated analytics; insufficient feedback and learning loops; weak leadership support |
| Excerpt | Interview excerpt | Expert IDs | Identified open codes |
|---|---|---|---|
| 1 | One of the main challenges in an Industry 4.0 environment is the absence of a clear strategic vision, which often results in misaligned and uncoordinated digital knowledge initiatives by organizations. Though BDA/AI generate massive volumes of data, much of it is not translated into actionable insights because of weak learning mechanisms and insufficiently skilled manpower. In addition, lack of coordination between human and Industry 4.0 technological systems results in the underutilization of employees' valuable tacit and experiential knowledge | R01, R02, R03, R04, R05, R10, R18 | Clear strategic vision; massive volume of data; weak learning mechanisms and unskilled manpower; human–AI collaboration |
| 2 | Owing to the absence of standardized semantic models and ontologies, the codification of tacit knowledge gained through experiential insights remains difficult. Also, production pressures limit the time and resources available for codifying digital and analytical insights gained from IoT devices. Insufficient integration of legacy and digital systems fragments the organizational knowledge base, while rapid technological change in the I4.0 environment quickly renders documented procedures obsolete | R04, R06, R12, R13, R17, R19 | Codification of tacit knowledge; standard semantic models and ontologies; time and resources; insufficient integration of legacy and digital systems; rapid technology change |
| 3 | Knowledge sharing within and across departments is hindered by organizational resistance and weak cross-functional collaboration. Poor knowledge transfer mechanisms across functional managers further limit knowledge exchange. In addition to interoperability constraints, limited trust, data security, and privacy concerns among employees constrain digital transfer and reduce sharing effectiveness | R01, R05, R06, R14, R15, R20 | Organizational resistance and weak cross functional collaboration; knowledge transfer mechanisms; interoperability constraints and limited trust; data security; and privacy concerns |
| 4 | Knowledge obtained from digital repositories or machine-generated analytics dashboards is often not integrated into the operational workflow. For instance, although production managers may have access to real-time machine performance data, they lack the tools or decision support systems to use it in scheduling or maintenance decisions. This underscores the need for infrastructure to effectively utilize and share generated knowledge. In addition, weak leadership support, insufficient feedback, and lack of learning loops hinder the effective utilization of AI generated knowledge across the departments | R02, R07, R08, R09, R11, R16 | KM and business processes misalignment; lack of tools or decision support systems; machine-generated analytics; insufficient feedback and learning loops; weak leadership support |
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