Search results
1 – 10 of over 163000Examines some principles of the design and implementation of decision support systems in the context of statistical quality control. These principles are a function both of the…
Abstract
Examines some principles of the design and implementation of decision support systems in the context of statistical quality control. These principles are a function both of the level within the organization at which the decision is made and of the type of decision that is made. The principles are well known in the field of decision support systems and have proved effective. Argues that these principles are of great value to researchers, managers and quality specialists in the field of statistical quality control as well. The argument is supported by a number of actual examples in the application of statistical quality control where knowledge of the principles proved of value in improving quality.
Details
Keywords
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…
Abstract
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.
Details
Keywords
Matteo Cristofaro, Christopher P. Neck, Pier Luigi Giardino and Christopher B. Neck
This study aims to investigate the relationship between shared leadership (SL) and decision quality, utilizing shared leadership theory (SLT) and behavioral decision theory (BDT)…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
Details
Keywords
Franziska Franke and Martin R.W. Hiebl
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…
Abstract
Purpose
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.
Design/methodology/approach
The authors draw on survey data from 140 US firms. This survey has been conducted via MTurk in 2020.
Findings
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.
Practical implications
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.
Originality/value
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.
Details
Keywords
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…
Abstract
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.
Details
Keywords
Nik Maheran Nik Muhammad, Muhamad Jantan, Fauziah and Taib
Previous studies on scanning behavior have focused mainly on decisions relating to the choice of corporate strategies, leaving strategic investment decisions largely understudied…
Abstract
Purpose
Previous studies on scanning behavior have focused mainly on decisions relating to the choice of corporate strategies, leaving strategic investment decisions largely understudied. This paper aims to bridge the gap not just by examining strategic investment decisions but also by investigating the role of information processing capacity in enhancing the relationship between the extent of scanning behavior and the quality of the investment decision.
Design/methodology/approach
Cross‐sectional data are collected through a survey and analyzed by means of factor analysis and hierarchical regression analysis.
Findings
Quality of decision is positively and significantly related to the extent of economic and competition information and the formality of method used to scan competition information. However, the extent of scanning for technology is contingent upon information processing capacity in order to affect the quality of the investment decision. Similarly, the method of scanning for economy, technology and competition information would depend on the information processing capacity to bring about a quality decision.
Research limitations/implications
Use of convenience sampling may restrict the generalizability of the findings.
Practical implications
As more economy and competition data are scanned, this would improve the quality of decision making, but for technology scanning the data have to be processed further before they can bring about changes in decision quality. For technology‐related matters, firms should be investing in the information processing capacity to produce quality decisions.
Originality/value
This study uses the decision as its unit of analysis to avoid having to average out the effects of making good and bad decisions often associated with a decision maker.
Details
Keywords
Qasim Ali Nisar, Nadia Nasir, Samia Jamshed, Shumaila Naz, Mubashar Ali and Shahzad Ali
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…
Abstract
Purpose
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.
Design/methodology/approach
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
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.
Practical implications
This study provides guidelines required by hospitals to strengthen their big data capabilities to improve decision-making quality and environmental performance.
Originality/value
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.
Details
Keywords
Moses Munyami Kinatta, Twaha Kigongo Kaawaase, John C. Munene, Isaac Nkote and Stephen Korutaro Nkundabanyanga
This study examines the relationship between investor cognitive bias, investor intuitive attributes and investment decision quality in commercial real estate in Uganda.
Abstract
Purpose
This study examines the relationship between investor cognitive bias, investor intuitive attributes and investment decision quality in commercial real estate in Uganda.
Design/methodology/approach
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.
Findings
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.
Practical implications
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.
Originality/value
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.
Details
Keywords
Satish Sasalu Maheswarappa, Bharadhwaj Sivakumaran and Arun G. Kumar
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
Details
Keywords
S.I. Lao, K.L. Choy, G.T.S. Ho, Y.C. Tsim and C.K.H. Lee
With the increasing concerns about food management, attention is placed on the monitoring of different potential risk factors for food handling. Therefore, the purpose of this…
Abstract
Purpose
With the increasing concerns about food management, attention is placed on the monitoring of different potential risk factors for food handling. Therefore, the purpose of this paper is to propose a system that helps facilitate and improve the quality of decision making, reduces the level of substandard goods, and facilitates data capturing and manipulation, to help a warehouses improve quality assurance in the inventory‐receiving process with the support of technology.
Design/methodology/approach
This system consists of three modules, which integrate the radio frequency identification (RFID) technology, case‐based reasoning (CBR), and fuzzy reasoning (FR) technique to help monitor food quality assurance activities. In the first module, the data collection module, raw warehouse and work station information are collected. In the second module, the data sorting module, the collected data are stored in a database. In this module, data are decoded, and the coding stored in the RFID tags are transformed into meaningful information. The last module is the decision‐making module, through which the operation guidelines and optimal storage conditions are determined.
Findings
To validate the feasibility of the proposed system, a case study was conducted in food manufacturing companies. A pilot run of the system revealed that the performance of the receiving operation assignment and food quality assurance activities improved significantly.
Originality/value
In summary, the major contribution of this paper is to develop an effective infrastructure for managing food‐receiving process and facilitating decision making in quality assurance. Integrating CBR and FR techniques to improve the quality of decision making on food inventories is an emerging idea. The system development roadmap demonstrates the way to future research opportunities for managing food inventories in the receiving operations and implementing artificial intelligent techniques in the logistics industry.
Details