Communication in the age of virtual reality

Increasing regulatory obligations to adapt and execute environmentally friendly operations make it critical for businesses to pursue strategies that can strengthen their competitive edge in the market. Academics and practitioners alike have recently gravitated toward exploring how knowledge acquisition activities might improve business outcomes. To address this growing research interest, this study investigates the critical roles of green knowledge acquisition in enhancing green knowledge management and green technology innovation activities in improving corporate environmental performance, positioning resource commitment as a moderator. The research model has been assessed using structural equation modeling with survey data from 283 Indian manufacturers, demonstrating that green knowledge acquisition significantly impacts green knowledge management and green technology innovation. The statistical findings also show that green technology innovation acts catalyzes the translation of green knowledge management into improved corporate environmental performance. The results demonstrate that resource commitment moderates green knowledge acquisition's interaction with green knowledge management and green technology innovation, providing practical insights enabling managers to focus on planning, allocating, and budgeting resources for effective green practices that can contribute to improving corporate environmental performance. 

How do green knowledge management and green technology innovation impact corporate environmental performance? Understanding the role of green knowledge acquisition

Big data is a relative term describing a situation where the volume, velocity and variety of data exceed an organizations storage or compute capacity for accurate and timely decision making . Big data is not a single technology but a combination of old and new technologies that helps companies gain actionable insight. Therefore, big data is the capability to manage a huge volume of disparate data, at the right speed, and within the right time frame to allow real-time analysis and reaction. As we note earlier in this chapter, big data is typically broken down by three characteristics: Volume:How much data Velocity:How fast that data is processed Variety:The various types of data Although its convenient to simplify big data into the three Vs, it can be misleading and overly simplistic. For example, you may be managing a relatively small amount of very disparate, complex data or you may be processing a huge volume of very simple data. That simple data may be all structured or all unstructured. Even more important is the fourth V:veracity. How accurate is that data in predicting business value? Do the results of a big data analysis actually make sense? Determining relevant data is key to delivering value from massive amounts of data. However, big data is defined less by volume which is a constantly moving target than by its ever-increasing variety, velocity, variability and complexity .

Big Data at Work: Dispelling the Myths, Uncovering the Opportunities

This study aims to investigate the effects of open innovation (OI) and big data analytics (BDA) on reflective knowledge exchange (RKE) within the context of complex collaborative networks. Specifically, it considers the relationships between sourcing knowledge from an external environment, transferring knowledge to an external environment and adopting solutions that are useful to appropriate returns from innovation.,This study analyzes the connection between the number of patent applications and the amount of OI, as well as the association between the number of patent applications and the use of BDA. Data from firms in the 27 European Union countries were retrieved from the Eurostat database for the period 2014–2019 and were investigated using an ordinary least squares regression analysis.,Because of its twofold lens based on both knowledge management and OI, this study sheds light on OI collaboration modes and highlights the crucial role they could play in innovation. In particular, the results suggest that OI collaboration modes have a strong effect on innovation performance, stimulating the search for RKE.,This study furthers a deeper understanding of RKE, which is shown to be an important mechanism that incentivizes firms to increase their efforts in the innovation process. Further, RKE supports firms in taking full advantage of the innovative knowledge they generate within their inter-organizational network.

/pdf/harvesting-reflective-knowledge-exchange-for-inbound-open-4enpatc2rx.pdf

Harvesting reflective knowledge exchange for inbound open innovation in complex collaborative networks: an empirical verification in Europe

The literature on leadership is increasingly supporting the power of digital leadership in promoting corporate innovation. In spite of this, digital leadership is a noticeable omission from the literature. As such, in this study, we developed a model based on a resource-based view and social information processing theory to examine the roles of digital entrepreneurial orientation and digital organizational culture in the relationship between digital leadership and exploratory innovation. We examined the moderating role of big data analytics capabilities according to a resource-based view and dynamic capability theory. Using a time-lagged survey data of 401 followers and 88 leaders, the results show that (a) digital leadership has a positive impact on exploratory innovation; (b) digital entrepreneurial orientation and digital organizational culture mediate the positive relationship between digital leadership and exploratory innovation; and (c) and mediating effect is positive moderated by big data analytics capabilities. Thus, in this study we are not only responding to the call to strengthen digitalization research in organizations but also further deepening our understanding of the path from digital leadership to exploratory innovation. These findings have theoretical implications for the literature on leadership and managerial implications for practitioners.

Digital leadership and exploratory innovation: From the dual perspectives of strategic orientation and organizational culture

Digital transformation is primarily studied through the lens of information technology and strategic management. There should be an integration of leadership concepts into this topic considering th...

Viewing Digital Transformation through the Lens of Transformational Leadership

Digital transformations help organizations revitalize business processes and customer relationships. Industry professionals and academicians recognize that leadership skills and technology skills a...

Student perceptions of leadership skills necessary for digital transformation

An application of the dynamic knowledge creation model in big data

\n The current research examines the combined role of proactive personality and political skill in job crafting and work engagement by integrating the job demands-resources (JD-R) model and trait activation theory. Self-reported survey responses collected from three samples – university students (study 1, N = 363) and panel data (study 2, N = 300 and study 3, N = 206) – were analyzed using the PROCESS macro. Results revealed that political skill strengthened the relationship between proactive personality and work engagement and between proactive personality and job crafting when trait activated. Furthermore, perceived supervisor support did not interact with the job crafting–work engagement relationship with trait activation, suggesting that proactive individuals rely on self-resources to improve engagement when presented with trait-relevant situational cues. The findings extend JD-R theory to offer the interplay of proactive trait and political skill in facilitating overall job crating. JD-R is identified as a contextual condition for trait activation.

A multi-study approach to examine the interplay of proactive personality and political skill in job crafting


Purpose
This paper aims to apply the theoretical perspective of job embeddedness to delineate how organizations could bundle and implement specific HRD practices that cater to fit, connections and the psychological costs of leaving to influence employees’ organizational commitment.


Design/methodology/approach
Using a dual-study approach, the current research uses survey responses collected from two samples of working adults to test the theorized framework using structural equation modelling.


Findings
Replicated results reveal that on-the-job embeddedness predicts affective commitment. There was no association between embeddedness at the community level and organizational commitment in either study.


Originality/value
This research offers a fresh perspective to explore the direct influence that embeddedness has on organizational commitment in the context of HRD practices.


An examination of job embeddedness and organizational commitment in the context of HRD practices

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