Innovative Job Performance in Digitally Enabled Workplaces: A Systematic Review and Bibliometric Synthesis of Individual-Level Innovative Work Behavior
DOI:
https://doi.org/10.62794/ijober.v4i4.545Keywords:
innovative job performance, innovative work behavior, work engagement, digital leadership, perceived organizational support, motivation, systematic literature review, bibliometric analysis, TCCMAbstract
Innovative job performance (IJP) and innovative work behavior (IWB) are examined across distinct leadership, work-design, motivational, knowledge-sharing, and digital-work literatures. This review combines systematic full-text screening, bibliometric mapping, and a Theory–Context–Characteristics–Methodology (TCCM) synthesis to examine the individual-level evidence. The documented Scopus search of 19 September 2026 returned 23,225 records before database filters; 182 journal articles published in 2023–2026 were retained for Bibliometrix/Biblioshiny analysis. The available retrieval archive contained 90 PDF instances, including four exact duplicates. Of 86 unique full-text reports screened, 43 met the focused eligibility criteria. The 182-record bibliometric dataset covered 91 sources and 603 unique author names, with recurrent terms involving innovative work behavior, engagement, leadership, psychological empowerment, and knowledge sharing. The focused synthesis identified distinct organizational resources, motivational mechanisms, knowledge processes, and digital capabilities associated with IJP/IWB in the included studies. Much of the primary evidence relied on cross-sectional designs, and the higher-education substream was geographically concentrated. Within the bounded 182-record export, “excellencism” was absent from the searched metadata fields, and no record directly compared Generation Z and Millennials within an IJP/IWB model. The resulting conceptual framework should be interpreted as an evidence-informed synthesis rather than a causally validated model. Future research should improve temporal, multisource, multilevel, and measurement-invariant testing.
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