Search results

This paper aims to investigate whether the implementation of generative artificial intelligence (GAI) impacts government functionality. The study will analyse GAI’s positive attributes across different dimensions to comprehensively understand its value proposition for public organisations. Furthermore, the paper will outline the strategic interventions required to integrate GAI effectively within the organisational context of government transformation.

This study measures “government functionality” and “GAI implementation” using abstract macro variables as a second-order formative model. It also includes first-order measurable micro-variables to better understand the concept. In addition, the study introduces “organisational context” as a moderating factor to explain the complex dynamics of integrating GAI to improve government functionality. The study proposes a conceptual framework, which was analysed using exploratory data analysis, with primary data collected through questionnaires.

The study finds a positive correlation between the implementation of GAI and improved government functionality. Furthermore, it found that organisational contextualisation significantly moderates this relationship. All the empirical outcomes align with the prescribed statistical thresholds, concluding that the articulated conceptual framework holds significance.

The study has significant implications for managers, researchers and anyone involved in making, implementing or evaluating decisions related to digital government through GAI. However, the study has limitations, including a limited sample size and contextualisation of the Indian public sector.

The study contributes to existing knowledge by showing that implementing GAI positively correlates with improving government functionality. It further highlights the significance of GAI implementation according to the specific organisational context.

The paper follows Jason Cope's (2011) vision of a holistic perspective on the failure-based learning process. By analyzing the research since Cope's first attempt, which is often fragmentary in nature, and providing novel empirical insights, the paper aims to draw a new comprehensive picture of all five phases of entrepreneurial learning and their interplay.

The study features an interpretative phenomenological analysis of in-depth interviews with 18 failed entrepreneurs. Findings are presented and discussed in line with experiential learning theory and Cope's conceptual framework of five interrelated learning timeframes spanning from the descent into failure until re-emergence.

The study reveals different patterns of how entrepreneurs experience failure, ranging from abrupt to gradual descent paths, different management and coping behaviors, and varying learning effects depending on the new professional setting (entrepreneurial vs non-entrepreneurial). Analyzing the entrepreneurs' experiences throughout the process shows different paths and connections between individual phases. Findings indicate that the learning timeframes may overlap, appear in different orders, loop, or (partly) stay absent, indicating that the individual learning process is even more dynamic and heterogeneous than hitherto known.

The paper contributes to the field of entrepreneurial learning from failure, advancing Cope's seminal work on the learning process and -contents by providing novel empirical insights and discussing them in the light of recent scientific findings. Since entrepreneurial learning from failure is a complex and dynamic process, using a holistic lens in the analysis contributes to a better understanding of this phenomenon as an integrated whole.