The aim of business process compliance (BPC) is to ensure that business processes are executed in accordance with a prescribed set of rules. In practice, the evidence would suggest that achieving this goal is challenging. Penalties and subsequent remediation costs in the Australian banking industry amounted to over A$10bn between 2017 and 2022. The research community has identified many challenges, but the industry perspective is typically missing from that research. The study takes advantage of recent events in the Australian banking industry that saw detailed regulatory reports made public, highlighting the challenges the industry faces trying to maintain compliance. The study supplements these reports with BPC-related insights from practitioners and consultancy groups to develop an industry perspective on the challenges. Consolidating the two perspectives presents a comprehensive view of BPC's challenges and the differing emphasis that each stakeholder group places on the challenges. Process mining may provide a pathway to reconciling these different perspectives. Both the BPC literature and industry have promoted process mining's potential to address the challenges. To explore this proposition, the study details the features of representative commercial process mining software and then maps these features to the challenges. The resultant conceptual map is used to analyze known and novel BPC challenges. It outlines the limitations that must be addressed, from both a research and industry perspective, to maintain and improve compliance.

PurposeSelecting which processes to improve plays a critical role in the first phase of the business process management lifecycle, but it is a step with known pitfalls. Decision-makers rely on subjective criteria and their knowledge of the alternative processes put forward for selection is often inconsistent. This leads to poor quality decision-making and wastes resources. The purpose of this paper is to examine the proposition that decision-makers armed with context-enriched criteria make more logical, better-quality decisions. The context in question is qualitative, sensitive to decision-making bias and politically charged.Design/methodology/approachWe applied a design-science approach, engaging 70 industry decision-makers through a combination of research methods to assess how different contextual configurations, in a hypothetical scenario adapted from the Australian banking industry, influenced and ultimately improved the quality of the process selection step.FindingsThe study highlights the impact of framing effects on context, and the need to adapt framing to decision-maker behavior and provides five guidelines to improve process selection effectiveness.Originality/valueProcess selection research to date has largely focused on quantitative evaluation techniques, with little attention paid to the role of context and the behavioral interplay of decision-making styles in practice.

Purpose This paper aims to examine how managers make non-generally accepted accounting principles (GAAP) exclusion decisions depending on the regulatory guidance provided and their motivations. Guidance detail is a double-edged sword: resolving uncertainty but risking rule-based compliance over principled judgment. Design/methodology/approach This paper uses the context of non-GAAP measures in reporting, given the history of Securities and Exchange Commission changes in guidance detail. Drawing on theories of epistemic motivation and process accountability, this paper manipulates the goal of management (informativeness vs. opportunism) and guidance detail to examine effects on management decisions to exclude an ambiguous charge. Findings The 2×2 between participants experiment with 132 managers reveals that more detailed guidance increases likelihood of exclusion of an ambiguous charge. This paper further finds that this exclusion is more likely when management is given an informativeness goal, a result of a mediating effect of epistemic motivation. However, these findings only hold at low levels of process accountability. Practical implications The findings regarding the psychological concepts recognize the influence of perceived decision uncertainty by suggesting how managers respond to the level of regulatory guidance detail, offering regulators and auditors a basis for understanding and anticipating managerial reporting choices. Also, awareness of heightened epistemic motivation under the informativeness goal provides a nuanced practical understanding of non-GAAP decision drivers. Finally, the finding that effects are more pronounced for managers with lower process accountability highlights the significance of organizational accountability structures in guiding managerial choices, which can inform board-level governance and control decisions. Originality/value Pragmatically, this paper finds that detailed guidance leads to more appropriate exclusion decisions under a goal of informativeness but finds no such evidence where the goal is opportunism. No prior study has examined how the level of detail in guidance affects managers’ disclosure choices.

To realize value from their wealth of digital data, organizations are investing in data-driven organizational initiatives—efforts in which they must draw expertise in data, algorithms, and visualization together with knowledge and skills in business domains such as marketing and human resources. However, they face the challenge of crossing the knowledge divide between analytics groups and business groups. Exploring relationships between the two groups in 37 data-driven organizational initiatives, we develop a configuration-based model that explains analytics and businessdomain knowledge integration through the lens of synergy. Our configurational analyses revealed five configurations of relationships between the two, which bring about two distinct change outcomes: “dedicated data groups” and “multidisciplinary teams” lead to the emergence of new datadriven ways to work, and “analytics institutionalization,” “analytics resource optimization,” and “networked communities” produce convergence, through the sharing of data-driven ways to work. Each configuration displays a distinct element of the core processes identified (“developing group connectedness,” “exchanging analytics and business domain knowledge,” and “incentivizing organizational data use”) and yields either an emergence or convergence of data-driven ways of working. The findings demonstrate how data-driven organizational initiatives can benefit from a pervasive form of organizing that entwines analytics groups and business groups such that their members’ tools, mindsets, and behaviors are merged to profoundly change ways of working. Together, these findings and the configurational methodology used provide a nuanced picture of how organizations integrate the requisite specialist knowledge across domains to realize value from data.

The business analytics and strategic management literatures suggest that organizations should seek to exploit data as a key mechanism for competitive advantage. However, the rules of engagement are evolving, the regulatory landscape is becoming increasingly complex, and examples of poor outcomes are increasingly common. The board – in its role of setting and monitoring risk appetite – needs to be able to govern the risk/reward trade-off of the data asset. Contemporary data governance approaches are inadequate: they are overly rigid and risk oriented, limited in scope to an organization's self-interest rather than considering the broad set of stakeholders, and do not provide a platform for the board to manage this critical risk. This paper uses a unique set of informants – 41 board directors – to demonstrate that differences in board perspectives influence how organizations explore the secondary use of data. Furthermore, this paper identifies a set of relevant individual, organizational and environmental factors and presents empirically based configurations of these factors that lead organizations to consider (or neglect) the secondary use of data as a critical enabler of competitive advantage.

As the first phase in the Business Process Management (BPM) lifecycle, process identification addresses the problem of identifying which processes to prioritize for improvement. Process selection plays a critical role in this phase, but it is a step with known pitfalls. Decision makers rely frequently on subjective criteria, and their knowledge of the alternative processes put forward for selection is often inconsistent. This leads to poor quality decision-making and wastes resources. In recent years, a rejection of a one-size-fits-all approach to BPM in favor of a more context-aware approach has gained significant academic attention. In this study, the role of context in the process selection step is considered. The context is qualitative, subjective, sensitive to decision-making bias and politically charged. We applied a design-science approach and engaged industry decision makers through a combination of research methods to assess how different configurations of process inputs influence and ultimately improve the quality of the process selection step. The study highlights the impact of framing effects on context and provides five guidelines to improve effectiveness.

Business Process Compliance (BPC) is a heavily researched domain in academia and one where significant progress has been made. However, there are known challenges. Some of these may be addressed by recent advances in process mining. To assess this proposition, we analyze progress across both fields of research in a banking context. Banks are heavily regulated and ensuring process compliance is expensive, complex and not guaranteed to succeed. In recent years, there have been a significant number of heavily publicized failings in Australia that point to the difficulties of maintaining business process compliance in practice. We proceed by first identifying evaluation criteria from a meta-review of the BPC literature and findings from the banking regulators, and then mapping the BPC studies to these criteria. Alongside, we present a meta-review of critical process mining capabilities and subsequently map these capabilities to the identified evaluation criteria. By leveraging the resulting conceptual map, we analyze known and novel BPC challenges, outlining the roadblocks to be addressed by BPC and process mining research to enable wide-scale deployment in practice.

Banks play an intrinsic role in any modern economy, recycling capital from savers to borrowers. They are heavily regulated and there have been a significant number of well publicized compliance failings in recent years. This is despite Business Process Compliance (BPC) being both a well researched domain in academia and one where significant progress has been made. This study seeks to determine why Australian banks find BPC so challenging. We interviewed 22 senior managers from a range of functions within the four major Australian banks to identify the key challenges. Not every process in every bank is facing the same issues, but in processes where a bank is particularly challenged to meet its compliance requirements, the same themes emerge. The compliance requirement load they bear is excessive, dynamic and complex. Fulfilling these requirements relies on impenetrable spaghetti processes, and the case for sustainable change remains elusive, locking banks into a fail-fix cycle that increases the underlying complexity. This paper proposes a conceptual framework that identifies and aggregates the challenges, and a circuit-breaker approach as an "off ramp" to the fail-fix cycle.

Big data analytics uses algorithms for decision-making and targeting of customers. These algorithms process large-scale data sets and create efficiencies in the decision-making process for organizations but are often incomprehensible to customers and inherently opaque in nature. Recent European Union regulations require that organizations communicate meaningful information to customers on the use of algorithms and the reasons behind decisions made about them. In this paper, we explore the use of explanations in big data analytics services. We rely on discourse ethics to argue that explanations can facilitate a balanced communication between organizations and customers, leading to transparency and trust for customers as well as customer engagement and reduced reputation risks for organizations. We conclude the paper by proposing future empirical research directions.

This is the second report of a project examining the introduction of AASB 16 Leases (AASB 16), the Australian equivalent of IFRS 16 Leases. In our first report1 we provided a snapshot of the preparer perspective of the process of AASB 16 implementation. In this report we provide results of our interview-based study of Australian professional investors, focussing on insights into the impact of the introduction of AASB 16 on investor decision making. This report provides a snapshot of investor views on the new standard to help highlight some of the key benefits and challenges associated with interpreting the new requirements. The report also aims to provide insights as to how investors are applying the information in their investment decision-making processes. Such insights are expected to be useful for practitioners and standard-setters in understanding the hurdles investors are currently facing in their use of the information provided under AASB 16 to make informed investment decisions. Together with our first report, this establishes a holistic perspective of AASB 16 as viewed by both preparers and investors, which can inform the standard-setters post-implementation reviews and the ongoing policy discussions.

The opaque nature of algorithms has led to negative consequences like discriminative and unfair decisions. Understanding these consequences requires consideration of algorithmic decision making from different stakeholder perspective (e.g. organizational vs. customer). We examine how explanations and evaluation metrics influence consequences of algorithmic decision making by prompting users to adopt different stakeholder perspectives. We specifically, examine the role of factual vs counterfactual explanations and framing of evaluation metrics impacts decision outcomes of choice, perceived fairness and confidence. We propose an experiment designed to test our hypotheses of the effects of counterfactual explanations and frames emphasizing false negatives rates on decision outcomes.

Anecdotal practitioner evidence indicates that organisations having long data histories and therefore the potential to compete through data are reluctant to do so, whilst emerging companies with relatively smaller existing asset base often seek to “win with data”. Underlying this apparent contradiction is the tension between the opportunity and risks involved in exploiting secondary use of data. In this research-in-progress paper, we explore the question: how do configurations of individual, organisational, and environmental factors influence board views of the opportunity and risk presented by secondary use of data? We develop a research framework based upon practitioner experience and the literature. We plan to refine the research framework based the results of focus groups of relevant experts - partners of professional services firms that advise boards in the strategic use of data. We then plan to test this practitioner verified framework through interviews with Australian board directors.

There has been recent and growing criticism of the usefulness of financial reporting for investors, particularly the annual financial statements. In response, the IASB is pursuing several projects aimed at improving the relevance of financial information. To inform the IASB’s work, we investigate, using a mixed‐method approach, the extent and nature of the use of annual financial statements by equity investors. We examine the relevance of financial reporting for equity valuation in Australia across time. We find that financial reporting (specifically, reported net income, shareholders’ equity, and operating cash flows) remains relevant for investment decisions. We further support this finding with evidence from field interviews that provide insight into how and why financial statements are used by equity investors. The field evidence also demonstrates that no one financial statement dominates in investor decision making. Given the increasing availability of more timely, forward‐looking information from alternative sources, we examine the relevance of non‐GAAP financial information and other non‐financial information for investor decision making. We find that non‐GAAP financial information (as proxied by EBIT and EBITDA) is more value relevant than statutory measures. We further find a broad range of non‐financial information is utilized by investors in making investment decisions both as a ‘screen’ and for valuation purposes. Our findings inform regulators and other stakeholders as we provide evidence of the continuing relevance of financial statements and the complementary role of non‐GAAP financial and other information. Our evidence provides a rebuttal to the recent criticism.

Using an experiment with participants having management experience, we examine sales forecast decisions when using an opaque versus transparent data analytics system. Participants have private information suggesting that the forecast significantly underestimates sales, making the forecast – and their bonus – easily achievable unless adjusted. We explore the extent to which participants act less ethically by not adjusting the sales forecast upwards. We employ a 2 x 2 between-subjects design, manipulating the description of the forecasting system as opaque or transparent, and measuring feelings of responsibility in the presence (absence) of a prompt before making the adjustment decision. We find that participants make less ethical decisions when the system is opaque than when it is transparent, but feelings of responsibility overcome this problem. We also find that the least ethical participants use both more rationalizations and more self-interested reasons than those whose decisions are not as unethical, supporting the use of both economic and psychology theory when studying ethical decision-making. Our results suggest that organizations should attempt to make data analytics systems more transparent to decision-making users. However, when they cannot, they should ensure that decision-makers feel responsible for their decisions; for example, with a prompt or decision aid.

PurposeThis study aims to examine the implementation of AASB 15 Revenue from Contracts with Customers to provide insight into preparers’ perspectives on the challenges, costs and benefits experienced in implementing a new and complex standard.Design/methodology/approachThe study uses a survey of 143 financial statement preparers engaged in implementing AASB 15.FindingsThe results reveal significant variation in the approach to, and progress in, implementing AASB 15.Research limitations/implicationsThe study provides evidence of the role of proprietary costs in implementing a new standard and suggests that preparers adopt a more pragmatic view of the nature of compliance compared to standard-setters.Practical implicationsThe evidence in this study strongly suggests that there is little to be gained in deferring effective dates for new standards. It suggests that standard-setters can motivate entities by framing a standard in terms of how it improves the business itself, rather than from a compliance framing.Originality/valueThis study provides a rare perspective on the actual implementation experience of preparers confronted with the introduction of a new standard. Such a perspective is of value to standard-setters and preparers and offers insight to researchers that cannot be gained from traditional capital market archival approaches.