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Based on a systematic literature review, requirements on a PPMS are identified in order to derive concrete demands and design features for such a system by using quality function deployment (QFD). The purpose of this paper is to formulate a proposal for design recommendations toward a holistic, customer-oriented Process Performance Measurement System (PPMS).

A literature review is used to identify customer demands and design features that characterize a PPMS. To determine the critical design features of a customer-oriented solution, the QFD method is applied.

The paper confirms that there is currently no published concept for an integrated, holistic PPMS. Therefore it provides a first approach to the formulation of a design recommendation based on the customer requirements and design features. A literature-based weighting facilitates a first identification of critical design features. The identified conditions specify the context which can be regarded as a prerequisite for the application of the system.

As a result of the investigation, two main issues were identified, which restrict the complete development of a House-of-Quality matrix and therefore require further research: First, no reliable relationships between the customer requirements and design features could be derived from the conducted content analysis and second, no correlations between the identified design features could be detected.

The paper provides a design basis for specific application systems and their information requirement analyses. It can also serve as an evaluation basis for existing software products in the market.

The connection of a literature review with the QFD procedure transfers a consolidated state of PPMS research into an applicable design recommendation and therefore supports rigor and relevance of the research.

The concept of information is central to several fields of research and professional practice. So many definitions have been put forward that complete inventory is unachievable while authors have failed to reach a consensus. In the face of the present impasse, innovative proposals could rouse information theorists to action, but literature surveys tend to emphasize the common traits of definitions. Reviewers are inclined to iron out originality in information models; thus the purpose of this paper is to discover the creativity of authors attempting to define the concept of information and to stimulate the progress of studies in this field.

Because the present inquiry could be influenced and distorted by personal criteria and opinions, the authors have adopted precise criteria and guidelines. It could be said the present approach approximates a statistical methodology.

The findings of this paper include (1) The authors found 32 original definitions of information which sometimes current surveys have overlooked. (2) The authors found a relation between information theories and advances in information technology. (3) Overall, the authors found that researchers take account of a wide variety of perspectives yet overlook the notion of information as used by computing practitioners such as electronic engineers and software developers.

The authors comment on some limitations of the procedure that was followed. Results 1 and 3 open up new possibilities for theoretical research in the information domain.

This is an attempt to conduct a bibliographical inquiry driven by objective and scientific criteria; its value lies in the fact that final report has not been influenced by personal choice or arbitrary viewpoints.

Why and how do cognitive distortions in managerial decision making occur? All organizations are imperfect systems (Katz and Kahn, 1966), with wrong decisions often just round the corner. As a consequence, addressing these important questions continues to be particularly lively in the management development area, especially in terms of its intended contribution to the de-biasing activity. Thus, the purpose of this paper is to provide the current scientific dialogue on the topic with updated lenses, which can also be innovative from some aspects.

The review framework is based on the recent, impactful article on biases in managerial decision making by Kahneman et al. (2011), and on Bazerman and Moore’s (2013) perspective on emanating heuristics, considered as the causes of biases. Accordingly, the authors derive four intertwined thematic clusters of heuristics, through which the authors systematically group and critically analyze the management literature mostly published on the topic since 2011.

From the analyzed clusters the authors propose an integrative framework of emanating heuristics, which focuses on the co-evolving relationships and potentially self-reinforcing processes in and between them.

The value of the contribution is threefold: from a methodological perspective, to the authors’ knowledge, the studies adopted as the basis of the analysis have not yet been simultaneously used as a comprehensive ground for updated reviews on this topic; from a conceptual perspective, the emerging integrative co-evolutionary framework can help explain the dangerous connections among cognitive traps and emanating heuristics; and from a practical perspective, the resulting framework can also be helpful for future de-biasing attempts in the business arena.

Grey target decision-making serves as a pivotal analytical tool for addressing dynamic multi-attribute group decision-making amidst uncertain information. However, the setting of bull's eye is frequently subjective, and each stage is considered independent of the others. Interference effects between each stage can easily influence one another. To address these challenges effectively, this paper employs quantum probability theory to construct quantum-like Bayesian networks, addressing interference effects in dynamic multi-attribute group decision-making.

Firstly, the bull's eye matrix of the scheme stage is derived based on the principle of group negotiation and maximum satisfaction deviation. Secondly, a nonlinear programming model for stage weight is constructed by using an improved Orness measure constraint to determine the stage weight. Finally, the quantum-like Bayesian network is constructed to explore the interference effect between stages. In this process, the decision of each stage is regarded as a wave function which occurs synchronously, with mutual interference impacting the aggregate result. Finally, the effectiveness and rationality of the model are verified through a public health emergency.

The research shows that there are interference effects between each stage. Both the dynamic grey target group decision model and the dynamic multi-attribute group decision model based on quantum-like Bayesian network proposed in this paper are scientific and effective. They enhance the flexibility and stability of actual decision-making and provide significant practical value.

To address issues like stage interference effects, subjective bull's eye settings and the absence of participative behavior in decision-making groups, this paper develops a grey target decision model grounded in group negotiation and maximum satisfaction deviation. Furthermore, by integrating the quantum-like Bayesian network model, this paper offers a novel perspective for addressing information fusion and subjective cognitive biases during decision-making.

The standard terms of time and space lead to logical contradictions near the borderline of physics. Therefore, we suggest a new term for time and space, which enlarges the understanding but takes the actual knowledge of quantum mechanics into account.

The procedure to define a broader and basic term of time and space is based on the relation between the information stored inside a system, the decision which is to be connected with it and the interaction with other systems.

Fundamental to this new understanding is a definition and an explanation of the terms system, decision and information. Based on these three terms we developed a new understanding of time and space. In which case, it involves a specifying and extension of the present understandings. A meaningful point is that these three terms determine each other as they are open terms. Thus, they must be used in the interactive description before and during their own definition.

Produces a new understanding and viewpoint which is enlarged by taking into account the knowledge of quantum mechanics.

The sandy environment is one of the typical environments in which helicopters operate. Air-sand two-phase flow in sandy environments may be an important factor affecting flight safety. Taking a typical example, this paper aims to investigate the aerodynamic and rotor trim characteristics of the UH-60 helicopter in sandy environments.

A computational study is conducted to simulate the air-sand flow over airfoils based on the Euler–Lagrange framework. The simulation uses the S-A turbulence model and the two-way momentum coupling methodology. Additionally, the trim characteristics of the UH-60 rotor are calculated based on the isolated rotor trim algorithm.

The simulation results show that air-sand flow significantly affects the aerodynamic characteristics of the SC1095 airfoil and the SC1094R8 airfoil. The presence of sand particles leads to a decrease in lift and an increase in drag. The calculation results of the UH-60 helicopter rotor indicate that the thrust decreases and the torque increases in the sandy environment. To maintain a steady forward flight in sandy environments, it is necessary to increase the collective pitch and the longitudinal cyclic pitch.

In this paper, the aerodynamic characteristics of airfoils and the trim characteristics in the air-sand flow of the UH-60 helicopter are discussed, which might be a new view to analyse the impact of sandy environments on helicopter safety and manoeuvring.

The purpose of this paper is to identify and describe the most prominent research areas connected with “Big Data” and propose a thorough definition of the term.

The authors have analysed a conspicuous corpus of industry and academia articles linked with Big Data to find commonalities among the topics they treated. The authors have also compiled a survey of existing definitions with a view of generating a more solid one that encompasses most of the work happening in the field.

The main themes of Big Data are: information, technology, methods and impact. The authors propose a new definition for the term that reads as follows: “Big Data is the Information asset characterized by such a High Volume, Velocity and Variety to require specific Technology and Analytical Methods for its transformation into Value.”

The formal definition that is proposed can enable a more coherent development of the concept of Big Data, as it solely relies on the essential strands of current state-of-the-art and is coherent with the most popular definitions currently used.

This is among the first structured attempts of building a convincing definition of Big Data. It also contains an original exploration of the topic in connection with library management.

The purpose of this paper is to develop, implement, test and further enhance a framework for measuring organizational change initiatives.

The conceptual part of the framework is based on the structured analysis of existing literature. The framework was further developed during an action research (AR) study where the authors developed, implemented, evaluated and improved the measurement system for organizational change initiatives.

The academic literature is rich in conceptual articles providing required characteristics of a “good” measurement system and frameworks for how organizations should measure performance. However, academia provides less empirical evidence of how these performance measurement systems can be implemented, evaluated and improved. In this paper, the authors present a study where the developed measurement system has been implemented, evaluated and improved. The results in terms of how the actual framework worked as well as the response from the case organizations are equally positive.

The framework has been implemented in two different, major change initiatives in one case organization. While the results are truly encouraging, the framework needs to be further tested and refined in more organizations.

There is a gap between academic perception and practical reality regarding how organizations should measure performance in general as well as measuring organizational change initiatives. The presented, and empirically tested, framework measures both the results of the change initiative (effectiveness) the actual change process (efficiency) as well as the perception of the change initiative and process from different key stakeholders.

This is the first developed, implemented and further improved measurement system for organizational change which measures both the efficiency and effectiveness of the change initiative (process).

The article's objective is to analyze the consequent factors of Big Data Analytics Capability for firms in the competitive scenario, using different analytical models.

The research had a quantitative approach, using a survey of data from firms located in the state of São Paulo – Brazil. Structural Equation Modeling (SEM) was used to validate the model.

The results reveal that all hypotheses were accepted. Business value was the construct that had the most explanatory power in the model. It is necessary to invest more in analytical tools, as well as people trained in the analysis of these models, in addition to a change of mindset, which will dictate the bias of the firm's strategic decision-making. The Big Data analysis is evident from firms' growing investments, particularly those that operate in complex and fast-paced environments.

The proposed theoretical model makes it possible to verify firms' analytical structure and whether they are better positioned to analyze customer data and information in real-time, generate insights and implement solutions to maintain and improve their market position.

The contribution of this article is to present a proposal to expand the research models in the literature that analyzed the direct and indirect relationship between “Big Data Analytics Capability” and “Product Innovation Performance”.