Unveiling heterogenous knowledge-oriented leadership and knowledge acquisition based hybrid work agility of knowledge workers

Abstract

Purpose With new hybrid working models in place post COVID-19, it is requisite that knowledge workers (KWs) stay agile. Knowledge-oriented leadership (KOL) can help employees with essential knowledge acquisition (KA) facilitating the journey toward hybrid work agility (HWA). This study, thus, aims to explore the impact of KOL and KA on HWA and reveal whether this effect stems uniformly from a single homogenous population or if there is unobserved heterogeneity leading to identifiable segments of agile KWs. Design/methodology/approach Data was collected through stratified sampling from 416 employees from 20 information technology enabled services companies involved in knowledge-intensive tasks. Partial least squares (PLS) structural equation modeling approach, using SMART PLS 4.0, has been applied to examine the effect of KOL and KA on HWA. Finite mixture PLS, PLS prediction-oriented segmentation and multigroup analysis have been used to identify segments, test segment-specific path models and analyze the significance of the differences in the path coefficients for unobserved heterogeneity. Predictive relevance of the model has been determined using PLS Predict. Findings Results indicate that KOL contributes to employees’ KA and HWA. A significant positive relationship is also reported between KA and HWA. The model has medium predictive relevance. A two-segment solution has been delineated, wherein independent agile KWs (who value autonomy and personal agency over leadership for KA) and dependent agile KWs (who depend on leaders for relational and structural support for KA) have been identified. Thus, KOL and KA play a differential role in determining HWA. Research limitations/implications The authors’ major contribution to the knowledge body constitutes the determination of antecedents of HWA and a typology of agile KWs. Future researchers may conduct segment-wise qualitative analysis to delineate other variables that contribute to HWA. Practical implications Technological advances necessitate that knowledge-intensive industries foster agility in employees for strategic agility of the organization. For effecting agile adaption of an organization to the knowledge economy conditions, it is pertinent that the full potential of this human resource be used. By profiling HWA of KWs on the basis of dimensions of KOL and the level of their KA, organizations will be able to help employees adapt better to rapidly changing work conditions. Originality/value HWA is a novel concept and very germane in a hybrid working environment. To the best of the authors’ knowledge, this is the first study to examine the effects of the dimensions of KOL and KA in relation to HWA, along with an empirical examination of unobserved heterogeneity in the aforementioned relationship.

Nexus of knowledge oriented leadership and knowledge worker performance in Chinese higher education institutions: a post-pandemic behavioral analysis

“…In addition, KOL provides training and development skills opportunities for employees to boost their knowledge and skills (G€ urlek and Cemberci, 2020). The COVID-19 emergency reshaped the labor market, compelling rapid adaptation and skill transfer to subsequent contexts, thereby increasing technological usage and workflow complexity (Khatri et al, 2023;Thomas and Gupta, 2022b). Moreover, F} uzi et al (2022) stated that digitalization is crucial for the survival of higher education institutions in the future.…”

“…The achievement-oriented leadership style sets ambitious goals to highlight excellent employee performance, confidence in tackling problems, and achieving the desired outcomes (Levine and Hogg, 2010). Digital citizenship is defined by Jones and Mitchell (2016) as “practicing respectful and tolerant behavior towards others while actively engaging in community activities.” Organizations focusing on technology-driven products prioritize workforce agility and flexible working models, with employees adapting to digital technology and hybrid environments being key to sustainability knowledge workers in post-COVID-19 (Khatri et al ., 2023). In addition, KOL provides training and development skills opportunities for employees to boost their knowledge and skills (Gürlek and Cemberci, 2020).…”

“…The COVID-19 epidemic has induced profound behavioral changes among personnel, particularly in the adoption of technology for daily activities (Hutasoit et al ., 2022). A hybrid work organization demands that workers acquire new knowledge and adapt to the post-pandemic period, which is a key driver for improving the performance of knowledge workers (Khatri et al ., 2023). Previous studies have examined the impact of different leadership styles on worker performance; for example, transformational leadership behavior significantly and positively influences employee performance (Ramadhanti et al ., 2021).…”

Fenntarthatósági átmenet: kihívások és innovatív megoldások : Nemzetközi tudományos konferencia a Magyar Tudomány Ünnepe alkalmából

“…A hibrid munkavégzés pedig nem más, mint a jelenléti, irodai munkavégzés és az otthoni munkavégzés keveréke, azaz, hogy a munkavállaló a munkaideje egy adott részében (heti vagy havi pár nap) otthonról dolgozik, a másik részében pedig az irodából (Khatri et al, 2023). A hibrid verzió a munkavállalói oldal leginkább kedvelt munkavégzési formája, mely a szakirodalom és a tapasztalatok szerint már velünk marad, de a munkáltatók és a munkavállalók még mindig tanulják, hogy hogyan is lesz sikeres a hibrid munkavégzési rendszer (Executive Networks, 2023).…”

Modeling intellectual capital-based intrapreneurial ability of working professionals through servant leadership and self-efficacy

“…After this step, an analysis of the prediction error's distribution provided in Histogram plots is done. Data's symmetry indicates the suitability of using the RSME values in place of MAE (Shmueli et al, 2019;Khatri et al, 2023). Table 6 represents that RSME values for a minority number of indicators have smaller prediction errors when compared to LM.…”