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The purpose of this paper is to create a model that connects drivers between organizational innovation and business model innovation (BMI) to guide empirical research and the design of innovation management strategies.

The model was designed based on the results of a systematic literature review over the past 25 years that provides common predictor variables to build bridges between these two types of innovations.

It is a conceptual relationship between organizational innovation and BMI based on processes, new structures and customer relationship management. Moreover, there are five bridges from common predictors: strategy, top management, exploratory learning, technological innovation and environmental complexity.

The relationships between organizational innovation and BMI have been neglected in the literature. The model fills this gap by proposing hypotheses for empirical research and critical variables and relationships to steer organizational and business model innovation.

This study aims to meticulously evaluate the public service value-generation process facilitated by collaborative e-governance services within the framework of the National e-governance Plan (NeGP).

The study formulates a comprehensive research model through a combination of literature review, insights from domain experts and hands-on experience gained from the e-governance project. A conceptual research model was meticulously structured, validated, and interpreted by using a reflective measurement theory. The analytical tool SmartPLS3 was used to assess the proposed model rigorously.

The analysis of collected data reveals a statistically significant positive correlation between the implementation of collaborative e-governance strategies and the creation of public service value. This relationship is further reinforced by a strong alignment between the perceived aspects of collaborative e-governance, such as responsiveness, transparency and service delivery and their substantial contribution to the enhancement of public service value.

This study contributes to the scholarly discourse by introducing an innovative methodology for assessing public service value through analyzing empirical data from citizen-centric collaborative e-governance projects. It is noteworthy that no prior studies have examined the nuanced concept of public service value in the context of collaborative e-governance.

This study aims to examine why tax reforms fail and explores how tax collection can be improved within a developing country context.

Data comprise 28 semi-structured interviews with taxpayers, tax experts and tax authority personnel based in Pakistan. The results are analysed using a combined lens of taxpayer trust and tax agencies’ capabilities.

Tax reforms failed to build taxpayers’ trust and tax agencies’ capabilities. Building trust is challenging and demands extensive ongoing engagement with taxpayers while yielding gradual permanent results. This requires enhancing confidence in government; educating taxpayers; removing complexities; introducing transparency and accountability in tax agencies’ operations and the tax system; promoting procedural and distributive justice; and reversing perceptions of corruption through reconciliation and stakeholder inclusivity. Developing tax agencies’ capabilities requires upgrading outdated technologies, systems and processes; implementing governance and organisational reforms; introducing an oversight board; and recruiting and training skilled professionals.

The findings can assist policymakers and tax collection authorities in understanding why tax reforms fail and identifying potential solutions.

This study contributes to the emerging literature by exploring tax administration failures in developing countries. It contributes to the literature by engaging stakeholders to understand why reforms fail and potential solutions to stimulate tax revenues.

This study aimed to evaluate the potential of artificial intelligence (AI) as a tool for knowledge synthesis, the production of written content and the delivery of coaching conversations.

The research employed the use of experts to evaluate the outputs from ChatGPT's AI tool in blind tests to review the accuracy and value of outcomes for written content and for coaching conversations.

The results from these tasks indicate that there is a significant gap between comparative search tools such as Google Scholar, specialist online discovery tools (EBSCO and PsycNet) and GPT-4's performance. GPT-4 lacks the accuracy and detail which can be found through other tools, although the material produced has strong face validity. It argues organisations, academic institutions and training providers should put in place policies regarding the use of such tools, and professional bodies should amend ethical codes of practice to reduce the risks of false claims being used in published work.

This is the first research paper to evaluate the current potential of generative AI tools for research, knowledge curation and coaching conversations.