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This paper aims to conduct a fuzzy discrete time cost quality risk in the ambiguous mode CO_{2 tradeoff problem} (FDTCQRP*TP) in a megaproject based on fuzzy ground.

A combinatorial evolutionary algorithm using Fuzzy Invasive Weed Optimization (FIWO) is used in the discrete form of the problem where the parameters are fully fuzzy multi-objective and provide a space incorporating all dimensions of the problem. Also, the fuzzy data and computations are used with the Chanas method selected for the computational analysis. Moreover, uncertainty is defined in FIWO. The presented FIWO simulation, its utility and superiority are tested on sample problems.

The reproduction, rearrangement and maintaining elite invasive weeds in FIWO can lead to a higher level of accuracy, convergence and strength for solving FDTCQRP*TP fuzzy rules and a risk ground in the ambiguous mode with the emphasis on the necessity of CO₂ pollution reduction. The results reveal the effectiveness of the algorithm and its flexibility in the megaproject managers' decision making, convergence and accuracy regarding CO₂ pollution reduction.

This paper offers a multi-objective fully fuzzy tradeoff in the ambiguous mode with the approach of CO₂ pollution reduction.

The gift-giving literature has documented several cases in which givers and recipients do not see eye-to-eye in gift-giving decisions. To help integrate this considerable segment of the gifting literature, this paper aims to develop a social norms-based framework for understanding and predicting giver-recipient asymmetries in gift selection.

Five experimental studies test the hypotheses. Participants in these studies evaluate gifts used in previous research, choose between gifts as either gift-givers or gift-recipients, and/or indicate their level of discomfort with choosing different kinds of gifts. The gifts vary in ways that allow the authors to test the social norms-based framework.

Gift-giving asymmetries tend to occur when one of the gifts under consideration is less descriptively, but not less injunctively, normative than the other. This theme holds for both asymmetries recorded in the gift-giving literature and novel ones. Indeed, the authors document new asymmetries in cases where the framework would expect asymmetries to occur and, providing critical support for the framework, the absence of asymmetries in cases where the framework would not expect asymmetries to emerge. Moreover, the authors explain these asymmetries, and lack thereof, using a mechanism that is novel to the literature on gift-giving mismatches: feelings of discomfort.

This research has multiple theoretical implications for the literatures studying gift-giving and social norms. A limitation of this work is that it left some (secondary) predictions of its model untested. Future research could test some of these predictions.

Billions of dollars are spent on gifts each year, making gift-giving a research topic of great practical importance. In addition, the research offers suggestions to consumers giving gifts, consumers receiving gifts, as well as marketers.

The research is original in that it creates a novel framework that predicts both the presence and absence of gift-giving asymmetries, introduces a psychological mechanism to the literature on giver-recipient gift choice asymmetries, and unifies many of the mismatches previously documented in this literature.

A visual sweetness scale with an arrow pointing to a specific sweetness level is now required on all labels of AOC Alsace. The sweetness scale makes it easier for consumers to understand what is in the bottle. What is less clear, however, is whether such labeling is always effective. To fill this gap, the current research paper aims to examine the positive and negative effects (double-edged effects) of a visual sweetness scale and identify the boundary condition.

Two studies were conducted using a 2 (cue type: scale vs text) by 2 (consumer type: novices vs experienced wine consumers) between-subjects, quasi-experimental design.

The double-edged effects are only significant among wine novices. Specifically, though wine novices are more likely to purchase wine with a sweetness scale (vs text) due to perceived diagnosticity (Study 1), they are unwilling to pay more due to low perceived quality (Study 2).

The study findings provide practical implications for wine producers, marketers and restaurants regarding when and how to use the sweetness scale on wine labels and wine service.

To the best of the authors’ knowledge, this research is the first to reveal the impact of visualizing wine style on wine labels. More importantly, while most previous research demonstrates the positive effects of using visual cues, this research sheds light on its drawbacks and examines the underlying mechanisms.

In this paper, the author aims to investigate the relationship between economic growth and unemployment in six Arab countries from Middle East and North Africa (MENA) zone including Tunisia, Egypt, Morocco, Lebanon, Jordan and Oman through the implementation of Okun's law using quarterly dataset covering the time period 2000: 1–2014: 4.

In this paper, static and dynamic linear and nonlinear models are used to test the linkage between cyclical unemployment and cyclical growth rate.

The empirical results from considered models confirm an inverse linkage between unemployment rate and economic growth, as the Okun's law suggests (except for Oman). In a nonlinear autoregressive dynamic linear (NARDL) framework and gap specification, statistically significant Okun's coefficients indicate that output growth can be translated into employment gains. Absolute effect of an economic contraction is significantly larger than that of an expansion in Tunisia, Egypt, Morocco and Lebanon. The opposite is true for Jordan and Oman.

Empirical finding provides then an additional proof that Okun's law could exist in a developing countries such as Tunisia, Egypt, Morocco, Lebanon and Jordan. Hence, any attempt to increase gross domestic product (GDP) through some economic fiscal and/or monetary policies in these countries would reduce unemployment rate.

Based on asymmetric specification, the author can conclude with precision that an economic upturn of 3.37, 2.98 and 2.5%, respectively, in Tunisia, Morocco and Egypt reduces unemployment by 1%, whilst the downturn of 5.03 and 2.43% (and about 12%), respectively, in Tunisia and Morocco (and Lebanon and Jordan) achieves the opposite.

Efficient management of earthmoving equipment is critical for decision-makers in construction engineering management. Thus, the purpose of this paper is to prudently identify, select, manage and optimize the associated decision variables (e.g. capacity, number and speed) for trucks and loaders equipment to minimize cost and time objectives.

This paper addresses an innovative multiobjective and multivariable mathematical optimization model to generate a Pareto-optimality set of solutions that offers insights of optimal tradeoffs between minimizing earthmoving activity’s cost and time. The proposed model has three major stages: first, define all related decision variables for trucks and loaders and detect all related constraints that affect the optimization model; second, derive the mathematical optimization model and apply the multiobjective genetic algorithms and classify all inputs and outputs related to the mathematical model; and third, model validation.

The efficiency of the proposed optimization model has been validated using a case study of earthmoving activities based on data collected from the real-world construction site. The outputs of the conducted optimization process promise the model’s originality and efficiency in generating optimal solutions for optimal time and cost objectives.

This model provides the decision-maker with an efficient tool to select the optimal design variables to minimize the activity's time and cost.

E-commerce companies use different types of dark patterns to manipulate choices and earn higher revenues. This study aims to evaluate and prioritize dark patterns used by e-commerce companies to determine which dark patterns are the most profitable and risky.

The analytic hierarchy process (AHP) prioritizes the observed categories of dark patterns based on the literature. Several corporate and academic specialists were consulted to create a comparison matrix to assess the elements of the detected dark pattern types.

Economic indicators are the most significant aspect of every business. Consequently, many companies use manipulative methods such as dark patterns to boost their revenue. The study revealed that the revenue generated by the types of dark patterns varies greatly. It was found that exigency, social proof, forced action and sneaking generate the highest revenues, whereas obstruction and misdirection create only marginal revenues for an e-commerce company.

The limitation of the AHP study is that the rating scale used in the analysis is conceptual. Consequentially, pairwise comparisons may induce bias in the results.

This paper suggests methodical and operational techniques to choose the priority of dark patterns to drive profits with minimum tradeoffs. The dark pattern ranking technique might be carried out by companies once a year to understand the implications of any new dark patterns used.

The advantages of understanding the trade-offs of implementing dark patterns are massive. E-commerce companies can optimize their spent time and resources by implementing the most beneficial dark patterns and avoiding the ones that drive marginal profits and annoy consumers.

This study is intended to motivate and guide future researchers to rethink and update their theories of operational capability development. By examining the extensive body of research on operational capabilities and working closely with an industry partner, the authors are iteratively developing new thinking about why our existing models seem to be failing and what aspects are likely to be useful in updating them.

This pathway paper is based on observations gained through a structured literature review, close collaboration with an industry partner and discussions with other industry partners and executives.

The authors identify ways in which the operations management community could begin to challenge and expand existing models of operational capability development. They provide reflections on the network structure of operational capabilities, i.e. their interconnectedness and interactions, which are likely to evolve dynamically over time and have not yet been part of the authors’ thinking about operational capability development.

The authors hope to stimulate new research through this pathway paper. By synthesizing their existing knowledge of operational capabilities and collaborating with an industry partner, the authors have attempted to highlight their limited knowledge of capability development. In addition, the authors offer several opportunities to rethink their existing models.

This paper aims to test the hedging ability of housing investment against inflation in Japan and the USA during the period 2000–2020.

This study applies the deep learning method and The exponential general autoregressive conditional heteroskedasticity in mean (1, 1) model with breaks.

Within the asymmetric framework, it is found that housing returns (HR) can hedge against inflation in both these markets, which mentions that when investing in the housing market in Japan and the USA, investors are compensated for bearing from inflation. This result is consistent with Fisher’s hypothesis. Especially, the empirical results show that the risk-return tradeoff is available in Japan’s housing market and not available in the US housing market. Any signal of a high inflation rate – referred to as “bad news” – may cause a drop in HR in Japan and a raise in the USA.

To the best of the author’s knowledge, this is one of the first studies using the deep learning method (long short-term memory model) to estimate the expected/unexpected inflation rates.

Alexandre Tombini, the governor of the Central Bank of Brazil, faced a difficult situation in July 2015. Inflation was in the double digits, well above the target rate of 4.5%, and unemployment had increased from around 4.5% a year prior to nearly 8%. Any actions Tombini took to control inflation would most likely exacerbate unemployment, at least in the short run. To further complicate matters, Tombini's office was not independent of the executive branch of Brazil's government, and Tombini faced the possibility that any of his actions that were not aligned with the priorities of the current administration could cost him his job.

This case follows classes on fiscal and monetary policy in normal times and is the first class in a sequence on macroeconomic challenges–in this case, stagflation–high inflation and high unemployment. Students are pushed to consider why macroeconomic stabilization involves such acute and unpleasant tradeoffs during episodes of high inflation and unemployment. Students use the IS/LM AD/AS model as a reference.

The aim of this study is to understand the environmental benefits and economic savings associated with adoption of circular economy in the construction sector. The research findings will support different stakeholders and decision makers to develop business models based on responsible consumption of resources and build sustainable business models.

The research uses mixed methodology wherein inventory for life cycle assessment and life cycle costing for environmental and economic impacts is based on primary data using on-site visits for qualitative and quantitative data.

Different types of land transportation infrastructures are compared for their environmental impacts. It is found that bridges have the highest environmental impacts as compared to tunnels, roads and railways. Further, the results affirm the environmental and economic benefits of adopting circular economy practices.

This is one of a kind research that compares the environmental and economic tradeoffs of adopting circular economy in different types of land transportation infrastructures.