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Without a formulated quality management policy and a direct lead from the chief executive, companies are unlikely to be able to effectively co‐ordinate quality‐related decision making; consequently, the approach to quality tends to be inspection orientated. Results of a study on how quality‐related decisions are made in six companies also suggests that the involvement of quality staff in design, purchasing and market feedback is vital, ensuring that quality‐related decision making is effective and consistent with policy.

This study aims to investigate the relationship between shared leadership (SL) and decision quality, utilizing shared leadership theory (SLT) and behavioral decision theory (BDT). The authors will explore the mediating role of “decision comprehensiveness” in the SL–decision quality linkage. Additionally, the authors will examine how individual “self-leadership” and “debate” among team members moderate the relationship between SL and decision comprehensiveness.

The authors tested the hypothesized moderated mediation model using a sample of 506 professionals employed in 112 research and development (R&D) teams, along with their direct managers from large Italian firms. To examine the relationships, the authors employed confirmatory factor analyses and path analyses. In order to address endogeneity concerns, the authors incorporated an instrumental variable, namely delegation, into the analysis.

SL positively influences decision quality, mediated by decision comprehensiveness, where teams include comprehensive information in decision-making. The level of debate among team members positively moderates the SL–decision comprehensiveness relationship. High levels of self-leadership can harm SL by reducing decision comprehensiveness, indicating a downside. However, low or moderate levels of self-leadership do not harm decision comprehensiveness and can even benefit SL.

This is the first work to investigate the relationship between SL and decision quality, shedding light on the mechanisms underlying this association. By integrating SLT and BDT, the authors provide insights into how managers can make higher-quality decisions within self-leading teams. Moreover, this research makes a distinct contribution to the field of self-leadership by delineating its boundaries and identifying a potentially negative aspect within the self-influence process.

Existing research on the relationship between big data and organizational decision quality is still few and far between, and what does exist often assumes direct effects of big data on decision quality. More recent research indicates that such direct effects may be too simplistic, and in particular, an organization’s overall human skills are often not considered sufficiently. Inspired by the knowledge-based view, we therefore propose that interactions between three aspects of big data usage and management accountants’ data analytics skills may be key to reaching high-quality decisions. The purpose of this study is to test these predictions based on a survey of US firms.

The authors draw on survey data from 140 US firms. This survey has been conducted via MTurk in 2020.

The results of the study show that the quality of big data sources is associated with higher perceived levels of decision quality. However, according to the results, the breadth of big data sources and a data-driven culture only improve decision quality if management accountants’ data analytics skills are highly developed. These results point to the important, but so far unexamined role of an organization’s management accountants and their skills for translating big data into high-quality decisions.

The present study highlights the importance of an organization’s human skills in creating value out of big data. In particular, the findings imply that management accountants may need to increasingly draw on data analytics skills to make the most out of big data for their employers.

This study is among the first, to the best of the authors’ knowledge, to provide empirical proof of the relevance of an organization’s management accountants and their data analytics skills for reaching desirable firm-level outcomes. In addition, this study thus adds to the further advancement of the knowledge-based view by providing evidence that in contemporary big-data environments, interactions between tacit and explicit knowledge seem crucial for driving desirable firm-level outcomes.

This study is undertaken to examine the antecedents and role of big data decision-making capabilities toward decision-making quality and environmental performance among the Chinese public and private hospitals. It also examined the moderating effect of big data governance that was almost ignored in previous studies.

The target population consisted of managerial employees (IT experts and executives) in hospitals. Data collected using a survey questionnaire from 752 respondents (374 respondents from public hospitals and 378 respondents from private hospitals) was subjected to PLS-SEM for analysis.

Findings revealed that data management challenges (leadership focus, talent management, technology and organizational culture for big data) are significant antecedents for big data decision-making capabilities in both public and private hospitals. Moreover, it was also found that big data decision-making capabilities played a key role to improve the decision-making quality (effectiveness and efficiency), which positively contribute toward environmental performance in public and private hospitals of China. Public hospitals are playing greater attention to big data management for the sake of quality decision-making and environmental performance than private hospitals.

This study provides guidelines required by hospitals to strengthen their big data capabilities to improve decision-making quality and environmental performance.

The proposed model provides an insight look at the dynamic capabilities theory in the domain of big data management to tackle the environmental issues in hospitals. The current study is the novel addition in the literature, and it identifies that big data capabilities are envisioned to be a game-changer player in effective decision-making and to improve the environmental performance in health sector.

This study examines the relationship between investor cognitive bias, investor intuitive attributes and investment decision quality in commercial real estate in Uganda.

A cross-sectional research survey was used in this study, and data were collected from 200 investors of commercial real estate in Uganda using a structured questionnaire. Hierarchical regression analysis was used to test the hypotheses derived under this study.

The results indicate that investor cognitive bias and investor intuitive attributes are positive and significant determinants of investment decision quality in commercial real estate. In addition, the two components of Investor cognitive bias (framing variation and cognitive heuristics) are positive and significant determinants of investment decision quality, whereas mental accounting is a negative and significant determinant of investment decision quality. For investor intuitive attributes, confidence degree and loss aversion are positive and significant determinants of investment decision quality, whereas herding behavior is a negative and significant determinant of investment decision quality in commercial real estate in Uganda.

For practitioners in commercial real estate sector should emphasize independent evaluation of investment opportunities (framing variation), simplify information regarding investments (Cognitive heuristics), believe in own abilities (Confidence degree), be risk averse (loss aversion) and avoid making decisions based on subjective visual mind (mental accounting) and group think/herding in order to make quality investment decisions. For policymakers, the study has illuminated factors such as provision of reliable information that ought to be taken into account when promulgating policies for regulation of the commercial real estate sector. This will help investors to come up with investment decisions which are plausible.

Few studies have focused on investor cognitive bias and investor intuitive attributes on investment decision quality in commercial real estate. This study is the first to examine the relationship, especially in the commercial real estate sector in a developing country like Uganda.

Many online merchants today have adopted web personalization in the form of personalized product recommendations (PPRs) to improve consumer’s decision quality. The purpose of this paper is to reveal the roles of PPRs on consumer decision quality in online shopping from the theoretical perspective of information load.

To explore the dual roles of PPRs on consumer decision quality, this paper develops a research model for it. A 2 (information load: high vs low) × 2 (web personalization: PPRs vs non-PPRs) between-subjects design is conducted to empirically test the model.

The results indicate that: first, information load can increase perceived information overload and decrease perceived information underload; second, PPRs can weaken (enhance) the positive (negative) effect of information load on perceived information overload (perceived information underload); third, both perceived information overload and perceived information underload are negatively associated with consumer’s decision quality.

This paper originally develops a research model that explains the roles of PPRs on consumer decision quality from the theoretical perspective of information load in the online shopping context, which could add new insights to the field of web personalization, especially the impact of web personalization on consumer decision making.

The purpose of this paper is to investigate returns to search (getting a better product and/or a lower price as a result of search) when consumers use/do not use recommendation agents (RAs). Specifically, it studies the effect of RAs/no RAs on decision quality, decision confidence and decision satisfaction taking into account subjective knowledge (SK) and involvement.

This paper employed two between-subjects factorial experimental designs with subjects searching for digital cameras in a simulated online digital camera store. The experiment was conducted with graduate students in Chennai, Bengaluru and Mysore in India.

Results of two online experiments showed that when consumers used RAs, low search led to better decision quality, whereas when consumers did not use RAs, medium search led to optimum decision quality. When consumers use RAs, SK had a U-shaped influence on the decision quality indicating that decision quality was the lowest for those with medium SK. When consumers did not use RAs, the effect of SK on decision quality was an inverted U-shape, indicating optimum decision quality for medium SK consumers. When consumers did not use RAs, subjects with high involvement made better choices, whereas when consumers used RAs, low involvement subjects made better choices. However, subjects who searched more had higher decision confidence and decision satisfaction even if their choices were not better.

The effect of RA vs no RA in conjunction with relevant consumer characteristics influencing decision quality of the consumer is demonstrated in this study. The findings have important managerial, consumer and theoretical contributions to make.

Recently, the increasing development of digital accounting systems has raised their effects on the quality of decision-making. Consequently, this research aims to evaluate the effects of digital accounting systems success factors on the advancement of decision-making quality in Jordanian banks.

The questionnaires were sent to 187 decision-makers who are actual users of digital accounting systems in Jordanian banks. A quantitative research approach was adopted to test the proposed research model based on the partial least squares-structural equation modeling method.

The empirical results of the current research revealed that data and information quality had a significant impact on the overall decision-making quality with the digital accounting systems, whereas system quality had an insignificant impact on it. The results empirical also confirmed that information quality has mediated the relationship between data and system quality and decision-making quality. Eventually, analytical decision-making culture has moderated the relationship between information quality and decision-making quality.

The current research will provide attractive implications and recommendations for practitioners, accounting managers and decision-makers about evaluating the effect of digital accounting systems on improving the decision-making quality in Jordanian banks.

An outcome bias occurs when performance is evaluated based upon the outcome of the decision rather than upon the quality of the decision itself. The purpose of this paper is to test experimentally whether advisors eliminating the uncertainty in the quality of decisions as a potential driver of the outcome bias can eliminate this bias in judgements. Additionally, the paper analyses whether such advisors can attenuate the emotional experience after decisions’ outcomes by supporting the cognitive understanding of these outcomes.

The paper employs a between-subject experimental setting where decision makers are asked to make investment decisions. The two variables manipulated were advice (receiving advice vs deciding without an advice) and state (loss vs neutral vs gain state). Participants were randomly assigned to each group. One group completed all tasks while receiving advice before making a final decision. Another group completed all tasks without any advice. After completing each investment task, participants were randomly assigned to one of three possible states that determine their payoff.

The results reveal that advisors eliminating the uncertainty in the quality of decision can eliminate the outcome bias in the judgements of decision quality, especially after bad outcomes. Nevertheless, after controlling for the perceived quality of the decisions, advised individuals show a greater emotional sensitivity to bad outcomes than non-advised decision makers. These observations suggest that advisors eliminating the uncertainty in the decision quality can improve the understanding that good decisions can lead to bad outcomes just by chance, but they are not able to prevent affective reactions after bad outcomes; on the contrary, they may even reinforce them.

The observation that, after bad outcomes, advised decision makers are less willing to decide the same way than non-advised decision makers is consistent with empirical findings on the risk-taking behaviour of self-directed and mutual fund investors. Self-directed investors are less likely to revise their decision and sell an investment at a loss than mutual fund investors (Chang et al., 2016). While investors might sell funds because they are unable to observe the decision process of the manager and use the outcome to judge the manager’s skills, this study shows that such learning from decision outcomes is not necessary for observing the risk-taking behaviour of the investors. Even if the decision process of the advisor is observable (as in this study), the decision makers’ willingness to decide the same way is influenced by the losses – an effect that goes beyond the assessed quality of advice as the results of this study show.

The results of this study have important implications for advisors aiming to maintain a positive relationship with their clients. Convincing clients that an advice is optimal supports their understanding that a good advice can have bad outcomes. However, this understanding may not prevent affective reactions after bad outcomes. On the contrary, the affective response after bad outcomes is even stronger with the advice than without it. Hence, advisors should address not only issues related to the quality of the provided advice, but also emotional aspects, which could be related to what clients expect from following the advice.

This study is one of the few that account for the possibility that the outcome bias may arise because there is uncertainty regarding the optimal choice. In particular, this paper uses a much more powerful criterion to define an optimal choice than the expected value criterion used in previous studies. The criterion represents a minimal requirement for rational behaviour in expected utility theory and many non-expected utility theories.

In every manufacturing company, irrespective of size, product and other variables, management are constantly involved in quality‐related decisions which have a direct effect on product quality. An analytical method is described for assessing an organisation's approach to quality management. On application, managers are provided with information on the reality of their quality assurance activities. An overview of some of the issues involved in decision making is provided, followed by an outline of the research methodology, and, finally, the “method” is presented with some results arising from its application.