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One of the most emerging areas of audit knowledge is sustainable quality, which can comprehensively cover the development of audit reports inclusiveness for stakeholders. The purpose of this study is to present the sustainable audit quality (SAQ) framework based on the UK Financial Reporting Council.

The paper discusses the main concepts in both spreadsheets and process mining. Using a concrete data set as a running example, the different types of process mining are explained. Where spreadsheets work with numbers, process mining starts from event data with the aim to analyze processes.

The results of the study showed that the SAQ is more effective based on the two criteria of going concern and disclosure of key audit items. It was also determined that, based on total interpretive structural modeling, the two themes of debt covenants disclosure and assessment of firms’ leverage ratio have a higher priority for SAQ in the Tehran Stock Exchange.

This paper contributes to the discussion on the use of data mining for microarray databases and specifically for studying the present SAQ framework based on the UK Financial Reporting Council. An area that has not been paid attention to by prior research studies, and to the best of the authors’ knowledge, this study is the first research that attempts to provide the SAQ framework.

This study aims to investigate whether consumers and small businesses can transition from disposable to reusable coffee cups, using a community social marketing intervention, led by a Social Purpose Organisation.

An emergent case study approach using multiple sources of data developed an in-depth, multifaceted, real-world context evaluation of the intervention. The methodology draws on citizen science “messy” data collection involving multiple, fragmented sources.

Moving from single-use cups to reusables requires collective commitment by retailers, consumers and policymakers, despite the many incentives and penalties applied to incentivise behaviour change. Difficult post-COVID economics, austerity and infrastructure gaps are undermining both reusable acceptance and interim solutions to our dependence upon disposables.

Although the non-traditional methodology rendered gaps and omissions in the data, the citizen science was democratising and inclusive for the community.

Our practical contribution evaluates a whole community intervention setting to encourage reusable cups, integrating multiple stakeholders, in a non-controllable, non-experimental environment in contrast to previous research. This paper demonstrates how small community grants can foster impactful collaborative partnerships between an SPO and researchers, facilitate knowledge-exchange beyond the initial remit and provide a catalyst for possible future impact and outcomes.

To assess the impact at both the outcome and the process level of the intervention, we use Pawson and Tilley’s realist evaluation theory – the Context Mechanism Outcome framework. The methodological contribution demonstrates the process of citizen science “messy” data collection, likely to feature more frequently in future social science research addressing climate change and sustainability challenges.

The study aimed to investigate consumers’ views on criteria to be claimed for organic processed foods and information to be communicated.

An online survey was carried out among 439 adults living in Italy, users of processed organic food, to gather opinions on criteria that processing of organic food should meet and on the terms that best define “careful” processing. Next, a conjoint design was applied to examine the effects of five independent factors on consumers' ideal concept of “organic”; these were potential information on packaging, processing, additives and product quality, and the type of food product. Three products with different processing level were selected: an ultra-processed and multi-ingredient product (vegetable burger), a processed product preserved by canning (peas in glass jar) and a minimally processed product (bagged salad).

The findings highlight that consumers attach more importance to the organic food carrier than the informational messages. Information on the processing and packaging follows, with messages on quality and on additives seemingly of minor importance. Three clusters of respondents were identified: those driven primarily by the type of organic food (24.6%), those placing more emphasis on product processing (21.3%), and a third larger cluster (54.1%) who expressed almost equal importance to all the factors considered. As for the processing of organic products, “eco-friendly” was the best message.

This paper offers insights into what best outlines the ideal concept of “processed organic food” as seen by organic food consumers, to be communicated to better guide their purchasing decisions.

Microfoundations have received increasing attention in several management disciplines. This study aims to outline the uniqueness of microfoundations research in innovation, look at where it comes from and where it is going and provide rich opportunities for future work.

To advance research in this area, this study conducted a systematic literature review combining mixed methods and creating a mapping framework to take stock of progress in the innovation microfoundations research field.

This study shows how distinct subfields have formed around key ideas expressed in subsets of seminal articles, shedding light on the relational nature of knowledge creation – uncovering these subfields’ characteristics, evolution and future trajectories.

This study develops a framework that reflects a critical analysis of the microfoundations of innovation within dos three research levels – individual, process and interaction and structure – and highlights the research gaps and potential research questions for future research that reflect the broad spectrum of approaches in the microfoundations of innovation literature.

The presented research explored artificial intelligence (AI) application in the learning and development (L&D) function. Although a few studies reported AI and the people management processes, a systematic and structured study that evaluates the integration of AI with L&D focusing on scope, adoption and affecting factors is mainly absent. This study aims to explore L&D-related AI innovations, AI’s role in L&D processes, advantages of AI adoption and factors leading to effective AI-based learning following the analyse, design, develop, implement and evaluate approach.

The presented research has adopted a systematic literature review method to critically analyse, synthesise and map the extant research by identifying the broad themes involved. The review approach includes determining a time horizon, database selection, article selection and article classification. Databases from Emerald, Sage, Francis and Taylor, etc. were used, and the 81 research articles published between 1996 and 2022 were identified for analysis.

The result shows that AI innovations such as natural language processing, artificial neural networks, interactive voice response and text to speech, speech to text, technology-enhanced learning and robots can improve L&D process efficiency. One can achieve this by facilitating the articulation of learning module, identifying learners through face recognition and speech recognition systems, completing course work, etc. Further, the result also shows that AI can be adopted in evaluating learning aptitude, testing learners’ memory, tracking learning progress, measuring learning effectiveness, helping learners identify mistakes and suggesting corrections. Finally, L&D professionals can use AI to facilitate a quicker, more accurate and cheaper learning process, suitable for a large learning audience at a time, flexible, efficient, convenient and less expensive for learners.

In the absence of any systematic research on AI in L&D function, the result of this study may provide useful insights to researchers and practitioners.

Digitalization, innovation and changing customer requirements drive the continuous improvement of an organization's business processes. IT demand management (ITDM) as a methodology supports the holistic governance of IT and the corresponding business process change (BPC), by allocating resources to meet a company's requirements and strategic objectives. As ITDM decision-makers are not fully aware of how the as-is business processes operate and interact, making informed decisions that positively impact the to-be process is a key challenge.

In this paper, the authors address this challenge by developing a novel approach that integrates process mining and ITDM. To this end, the authors conduct an action research study where the researchers participated in the design, creation and evaluation of the approach. The proposed approach is illustrated using two sample demands of an insurance claims process. These demands are used to construct the artefact in multiple research circles and to validate the approach in practice. The authors applied learning and reflection methods for incrementally adjusting this study’s approach.

The study shows that the utilization of process mining activities during process changes on an operational level contributes to (1) increasing accuracy and efficiency of ITDM; (2) timely identification of potential risks and dependencies and (3) support of testing and acceptance of IT demands.

The implementation of this study’s approach improved ITDM practice. It appropriately addressed the information needs of decision-makers and unveiled the effects and consequences of process changes. Furthermore, providing a clearer picture of the process dependencies clarified the responsibilities and the interfaces at the intra- and inter-process level.

In light of the current challenges in the business environment, firms, particularly those involved in supply chains, must foster innovation. In this context, the current study employs the theory of dynamic organizational capabilities (DOCs) to track supply chain learning capabilities (SCLCs) and independently uncover their relationship to innovation at both the product and process levels. Similarly, the study intends to investigate the influence of technological turbulence (TT) on these relationships as a moderating variable.

The data were gathered using in-person interviews with 189 CEOs with some supply chain management proficiency from small and medium-sized enterprises (SMEs) in the industrial zones of eastern Egypt. The study used a survey approach to collect data, and the SEM-PLS technique was utilized to analyze the data.

Study findings revealed that SCLCs positively affect product and process innovation. In addition, TT positively moderates the relationship between SCLCs and product and process innovation, except for risk-taking capability. Further theoretical and practical implications are derived from the study findings.

This research adds to the knowledge of the dynamic capabilities theory (DCT), which affects how firms interact with their external environment. Studying learning capabilities are employed as essential competencies to counterbalance high levels of TT in the external environment in terms of innovative performance and vice versa if firms do not attempt to strengthen their dynamic learning capabilities in supply chains. In addition, this study contributes to the literature by studying learning capabilities from the external perspective, where SCLCs are being developed as a new variable to improve innovation.

Enabling and encouraging entrepreneurial activity has long been of interest to scholars and practitioners. Previous research is partly based on the theory of planned behavior. This research has consistently shown that personal attitudes and perceived behavioral control are correlated with entrepreneurial intentions. However, previous findings have found the relationship between subjective norms and entrepreneurial intentions to be inconsistent. This paper proposes a resolution to these discrepancies and tests these hypotheses in a university setting.

Using nearly 1,000 student survey responses, this paper uses an inter subject linear regression model to test elements of the theory of planned behavior that have been inconsistent in the literature.

This paper finds that personal attitudes and perceived behavioral control mediate or partially mediate the relationships between subjective norms and entrepreneurial intentions. Extension for theory development and practice are discussed.

While very insightful, this paper is limited by the use of a within-subjects survey research design and the inherent limitations with such research. Additionally, using college students as respondents limits the ability to generalize the results to any population outside similar college-age populations.

This paper provides a unique test of a set of controversial findings in research on the theory of planned behavior. Identifying unique mediators between social norms and entrepreneurial intentions expands this theory and provides fertile opportunities for future research.

This paper proposes a progressive, multi-level framework for diagnosing maintenance performance: rapid performance health checks of key performance for different equipment groups and end-to-end process diagnostics to further locate potential performance issues. A question-based performance evaluation approach is introduced to support the selection and derivation of case-specific indicators based on diagnostic aspects.

The case research method is used to develop the proposed framework. The generic parts of the framework are built on existing maintenance performance measurement theories through a literature review. In the case study, empirical maintenance data of 196 emergency shutdown valves (ESDVs) are collected over a two-year period to support the development and validation of the proposed approach.

To improve processes, companies need a separate performance measurement structure. This paper suggests a hierarchical model in four layers (objective, domain, aspect and performance measurement) to facilitate the selection and derivation of indicators, which could potentially reduce management complexity and help prioritize continuous performance improvement. Examples of new indicators are derived from a case study that includes 196 ESDVs at an offshore oil and gas production plant.

Methodological approaches to deriving various performance indicators have rarely been addressed in the maintenance field. The proposed diagnostic framework provides a structured way to identify and locate process performance issues by creating indicators that can bridge generic evaluation aspects and maintenance data. The framework is highly adaptive as data availability functions are used as inputs to generate indicators instead of passively filtering out non-applicable existing indicators.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.