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This paper aims to establish if quantified self-data positively impact motivation in a goal pursuit across a broad cross-section of consumers and in multiple contexts; and to understand the underlying causal mechanism and identify boundary conditions.

Exploratory qualitative research helped direct the hypotheses development. Two quantitative experiments were then conducted via MTURK, involving 331 respondents, to test the hypotheses in two different personal goal areas (fitness and carbon footprint reduction).

Self-quantification has a significant and positive impact on anticipated motivation in both contexts studied. The mediated model provides insight into the psychological process underlying self-quantification’s motivational impact, which involves strengthening user perceptions regarding feedback meaningfulness, self-empowerment and goal focus. Age (>50) was found to be a boundary condition; however, distance to goal was not.

This paper focuses on initial (anticipated) motivation, which is the vital first step in behavior change. However, more work is needed to understand quantification’s long-term impact over the course of a behavior change process.

This research encourages firms to incorporate self-quantification features into products/services aimed at behavior change and helps firms better understand consumer-perceived benefits. It alerts firms regarding the extra effort needed to convince older consumers of these benefits.

This is the first study to confirm the “quantification effect” on motivation in multiple life areas and provide a causal model to explain how it works. It is also the first to highlight age as a boundary condition.

Weather events have a significant impact on airport arrival performance and may cause delays in operations and/or constraints in airport capacity. In Europe, almost half of all regulated airport traffic delay is due to adverse weather conditions. Moreover, the closer airports operate to their maximum capacity, the more severe is the impact of a capacity loss due to external events such as weather. Various weather uncertainties occurring during airport operations can significantly delay some arrival processes and cause network-wide effects on the overall air traffic management (ATM) system. Quantifying the impact of weather is, therefore, a key feature to improve the decision-making process that enhances airport performance. It would allow airport operators to identify the relevant weather information needed, and help them decide on the appropriate actions to mitigate the consequences of adverse weather events. Therefore, this research aims to understand and quantify the impact of weather conditions on airport arrival processes, so it can be properly predicted and managed.

This study presents a methodology to evaluate the impact of adverse weather events on airport arrival performance (delay and throughput) and to define operational thresholds for significant weather conditions. This study uses a Bayesian Network approach to relate weather data from meteorological reports and airport arrival performance data with scheduled and actual movements, as well as arrival delays. This allows us to understand the relationships between weather phenomena and their impacts on arrival delay and throughput. The proposed model also provides us with the values of the explanatory variables (weather events) that lead to certain operational thresholds in the target variables (arrival delay and throughput). This study then presents a quantification of the airport performance with regard to an aggregated weather-performance metric. Specific weather phenomena are categorized through a synthetic index, which aims to quantify weather conditions at a given airport, based on aviation routine meteorological reports. This helps us to manage uncertainty at airport arrival operations by relating index levels with airport performance results.

The results are computed from a data set of over 750,000 flights on a major European hub and from local weather data during the period 2015–2018. This study combines delay and capacity metrics at different airport operational stages for the arrival process (final approach, taxi-in and in-block). Therefore, the spatial boundary of this study is not only the airport but also its surrounding airspace, to take both the arrival sequencing and metering area and potential holding patterns into consideration.

This study introduces a new approach for modeling causal relationships between airport arrival performance indicators and meteorological events, which can be used to quantify the impact of weather in airport arrival conditions, predict the evolution of airport operational scenarios and support airport decision-making processes.

Delay and disruption claims involve a complex process that often result in disputes, unnecessary expenses and time loss on construction projects. This study aims to review and synthesize the contributions of previous research undertaken in this area and propose future directions for improving the process of delay and disruption claims.

This study adopted a holistic systematic review of literature following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. A total of 230 articles were shortlisted related to delay and disruption claims in construction using Scopus and Web of Science databases.

Six research themes were identified and critically reviewed including delay analysis, disruption analysis, claim management, contract administration, dispute resolution and delay and disruption information and records. The systematic review showed that there is a dearth of research on managing the wide-ranging information required for delay and disruption claims, ensuring the transparency and uniformity in delay and disruption claims’ information and adopting an end-user’s centred research approach for resolving the problems in the process of delay and disruption claims.

Complexities in delay and disruption claims are real-world problems faced by industry practitioners. The findings will help the research community and industry practitioners to prioritize their energies toward information management of delay and disruption claims.

This study contributes to the body of knowledge in delay and disruption claims by identifying the need for conducting more research on its information requirements and management. Subsequently, it provides an insight on the use of modern technologies such as drones, building information modeling, radio frequency identifiers, blockchain, Bigdata and machine learning, as tools for more structured and efficient attainment of required information in a transparent and consistent manner. It also recommends greater use of design science research approach for delay and disruption claims. This will help to ensure delay and disruption claims are the least complex and less dispute-prone process.

The purpose of this paper is to investigate how management attention and supply chain complexity affect the decision-making process and cost estimation accuracy of supply chain design (SCD) decisions.

The research follows an embedded case study design. Through the lens of the behavioural theory of the firm, the SCD decision process and realised outcomes are investigated through longitudinal data collection across ten embedded cases with varying degrees of supply chain decision-making complexity and management attention.

The findings suggest that as supply chain decision-making complexity increases, cost estimation accuracy decreases. The extent to which supply chain decision-making complexity is readily recognised influences the selection of strategies for information search and analysis and, thus, impacts resulting cost estimation errors. The paper further shows the importance of management attention for cost estimation accuracy, especially management attention based on conflicting goals induce behaviours that improve estimation ability.

A framework proposing a balance between supply chain decision-making complexity and management attention in SCD decisions is proposed. However, as an embedded case study the research would benefit from replication to externally validate results.

The method used in this study can identify how supply chain complexity is related to cost estimation errors and how management attention is associated with behaviours that improve cost estimation accuracy, indicating the importance of management attention in complex supply chain decision-making.

The criteria selection of green suppliers is an interesting and practical research topic. With the development and consciousness of environmental management, this issue has received a significant consideration from academics, researchers and businessmen. The purpose of this paper is to study what factors are considered to be the most important when choosing partners/suppliers in a green supply chain, with reference to the Taiwanese hand tool industry, as this industry plays a major role in the Taiwanese economy.

Inheriting from previous researches, the literature about green supplier selection that appeared in international journals from 1997 to 2009 was reviewed. Then, the integrating fuzzy analytical hierarchy process (FAHP) approach is proposed to select the most important criteria for green supplier selection in the Taiwanese hand tool industry.

Ultimately, a list of criteria, along with their priority levels, is presented, not only to support Taiwanese hand‐tool companies when selecting suppliers, but also to help the suppliers of these companies to improve their performance in order to meet customers' (hand‐tool companies) requirements.

The approach used in this paper to select green suppliers can be applied to other industrial or geographical settings.

The primary purpose of this exploratory paper is to propose a novel analytical framework for examining corruption from a behavioral perspective by highlighting multiple issues associated with quantification culture.

This paper is part of the author’s exploratory study into the complex corruption phenomenon in Indonesia from a variety of viewpoints to obtain a better understanding of its nature and dynamics. By building on previous literature on quantification culture, audit culture and the corruption phenomenon, this paper explores the different issues related to overreliance on numbers and how they may have led to increased corruption, particularly in Indonesia. Using the Fraud Decision Scale framework, this paper also demonstrates how the quantification culture affects the cost-benefit analysis of corruption offenders. To develop the best anticorruption strategy and lay the groundwork for future anticorruption studies, this paper presents a fresh perspective on how to analyze and evaluate the corruption issue.

The author established that despite the benefits that quantification practices – such as indicators, scores, ratings and rankings – bring to societies and organizations, there is evidence that these practices can have unfavorable effects when used excessively or incorrectly. One of the adverse effects of quantification practices is over-quantification, which manifests as various inappropriate behaviors, including an excessive desire to acquire riches and material belongings. Additionally, the worship of ranks and status derived from quantitative evaluation processes has led many people to prioritize short-term objectives above long-term improvements. Eventually, these will cause organizations to lose productivity and make them susceptible to fraud and corruption. Future studies are needed to determine the most effective approach for mitigating over-quantification issues. In this exploratory paper, the author proposes balancing quantitative practices with qualitative knowledge to gradually alter people’s behavior by broadening their perspectives in perceiving various phenomena in the world, not only by describing and explaining them but also by comprehending their underlying meanings.

This exploratory study is self-funded and relies primarily on documentary analysis to explore the corruption phenomenon in Indonesia. Future studies will benefit from in-depth interviews with former corruption offenders and investigators.

This exploratory paper contributes to the development of a sound corruption prevention approach by presenting a novel analytical framework for examining various behavioral problems linked with quantification culture that may lead to the escalation of corruption.

This study emphasizes the significance of understanding the structure and dynamics of quantification culture and their negative behavioral impacts on people to comprehend the corruption phenomenon better.

The purpose of this paper is to present a novel hybrid fuzzy-system dynamics (SD) approach for the quantification of the impacts of construction claims.

The most significant claims affecting a project are identified. The various factors affecting the impacts of claims are identified. Then, the qualitative model of construction claims is constructed considering the complex inter-related structure of the influencing factors. The mathematical relationships among the variables are determined and the quantitative model of claims is built. Finally, fuzzy logic is integrated into the proposed model to take into account the existing uncertainties.

To show the capabilities of the proposed simulation model, it is implemented on a real project and the impacts of the identified claims on the project cost are quantified. It is shown that the external interactions among different claims can intensify their overall impact.

Identification of interactions among various influencing factors is not an easy job when there are a large number of claims in a project. Well-qualified experts and the existence of historical data may limit the application of the proposed method in projects with limited data and/or qualified experts.

The proposed hybrid fuzzy-SD approach provides a practical and flexible tool that can be used in various construction projects to assess the cost impacts of construction claims taking into account their complex interactions. Using the proposed method, the accuracy of achieved results is increased compared to conventional methods that are used for the quantification of claims since the complex inter-related structure of influencing factors and the claims interactions are taken into account. One of the capabilities of the proposed hybrid fuzzy-SD method is its flexibility. Depending on the type of contract and the parties involved in the project, the proposed hybrid fuzzy-SD method can be used during different stages of the project life cycle to model and quantify claims.

The proposed approach may present a flexible and robust method for quantification of construction claims. The novelty aspects of this paper are as follows: the extensively complex structure of claims arising from both internal and external interactions is accounted for using SD. The existing uncertainties affecting the impacts of a claim are taken into account.

The purpose of this paper is to present a methodology for assessing, in quantifiable terms, the reduction in environmental impacts achieved by applying different credits of sustainability rating systems in building construction projects.

Sustainability rating systems are developed in various regions to evaluate construction projects with respect to their environmental performance. Although implementation of rating systems had a recognized effect on reducing environmental impact of construction projects, there is no objective and quantifiable evidence that the approaches recommended by these rating systems to achieve the required certification lead to optimum environmental results. This paper presents a methodology that utilizes life cycle analysis (LCA) as a powerful and objective tool to validate the way rating systems evaluate project performance. The Qatar Sustainability Assessment System (QSAS), recently developed in the State of Qatar by Gulf Organization for Research and Development (GORD), is chosen as a case study to illustrate application of the developed methodology. Environmental impacts due to implementation of QSAS credits are calculated for one project in Qatar, which is currently under construction.

Results reveal possible use of LCA as a tool for evaluating the effectiveness of rating systems. For the QSAS case study, findings reveal indications of over and, in some instances, under estimation of the weights assigned to some credits and the difficulty in the quantification of the impacts of other credits, which indicates the need for reconsideration of these weights to improve effectiveness of the implementation of these credits.

The proposed methodology stands as a step toward the enhancement and rationalization of the currently used building sustainability ratings system.

The current study implies self-quantification to consumer behavior and investigates how self-quantification influences consumers' persistence intentions, then indicates the underlying mechanism and examines the role of sharing in social media context.

The hypotheses are tested by three experimental studies. In study 1, the authors test the main effect of self-quantification on persistence intentions and demonstrate goal specificity as the mediator. In study 2 and 3, the authors explore sharing and sharing audience as the moderators.

The current research demonstrates that quantifying personal performance increases consumers' persistence intentions because self-quantification makes the focal goal more specific. However, sharing self-quantification performance with others has a negative effect on the relationship between self-quantification and persistence intentions. Building on goal conflict theory, sharing diverts consumers' focus away from the goal itself and toward others' evaluation and judgment, which makes the focal goal more ambiguous. Moreover, the negative effect depends on who is the sharing audience. When consumers share with close others who hold a similar goal with them, the negative effect of sharing is dramatically reversed.

The present research offers guidelines to managers about how to design self-tracking system to increase user's engagement and how to establish social community on social media platform to motivate users' goal pursuit.

This study contributes to the research of self-quantification from consumer behavior perspective. It also enriches interactive marketing literature by broadening self-quantification relevant research from social interaction dimension.

This article addresses purchasing decisions and the use of total cost of ownership (TCO) information. TCO is based on a monetary quantification of nonfinancial attributes and aggregation into a summary measure (such as cost per hour, per wafer, or per kilometer). From an accounting point-of-view, one intricate issue is the accuracy of the monetary quantification and how this affects decision-making. We distinguish three different kinds of inaccurate monetary quantification, and we investigate the weight that decision makers attach to attributes that are inaccurately monetarily quantified and subsequently included in TCO information. Specifically, we investigate whether this weight depends on reflective thinking and experience. This question is relevant beyond TCO, for all decision-making situations that involve monetary quantification of attributes and subsequent aggregation, such as in activity-based costing, net present value calculations for capital budgeting decisions, or cost-benefit analyses in public administration.

We found support for the hypothesis that reflective thinking increases the weight decision makers attach to the attribute that is included as a minimum cost in the TCO-numbers, but not for the hypothesis that reflective thinking would reduce the weight attached to the attribute that is included as a maximum cost in the TCO-numbers. Students and practitioners differed significantly in the weight they attached to an attribute that was excluded from the TCO-numbers, and practitioners gave less weight to such attributes. Together these results suggest that TCO-numbers should be provided with care and possible inaccuracies should be clarified.