Ali Bonyadi Naeini

This article studies how package shape affects consumers' eye movements and product selection for liquid soap products, using eye‐tracking data and a choice experiment. The study involved 54 participants with different backgrounds, who chose their preferred product from pairs of photos of different packages. The results showed that package shape influenced consumer attention and choice, and that gender also had an effect on some of the measures. Packages with higher perimeter, eccentricity, and extent were more attractive and likely to be chosen by consumers, while packages with higher area, convex area, and roundness were less attractive and likely to be chosen. This article provides empirical evidence on the effect of package shape on consumer behavior for liquid soap products.

This study focuses on wind energy potential assessment using the Fuzzy Cognitive Mapping (FCM) method. Given the limitations of fossil resources and their negative environmental impacts, the use of renewable energy sources, especially wind energy, has become an essential necessity. This research emphasizes the assessment of wind energy potential through an analytical fuzzy model that can effectively manage the complexities of energy systems. In this study, 13 key concepts were identified, including economic growth, energy prices, return on investment, investment, demand management, and other important economic, social, and environmental factors. Data were collected through structured interviews with industry experts and the analysis of standard questionnaires, and were subsequently analyzed using the FCM model. The results of this analysis show that factors such as investment levels in wind projects, economic sustainability, and increased energy supply security have significant impacts on the development of wind energy. Additionally, the results highlight the importance of using various scenarios to predict future developments and evaluate strategic decisions in the field of renewable energy. Finally, two scenarios for wind energy development in the country were presented, specifically focusing on investment and supply security. This research not only provides comprehensive analysis of wind energy potential but also offers a conceptual framework for planning and decision-making in the development of renewable energy.

Background: The global prevalence of smoking remains a significant public health concern, necessitating the development of effective anti-smoking campaigns to promote smoking cessation and discourage smoking initiation. Understanding the factors that influence individual responses to anti-smoking messages is critical for tailoring campaigns to different populations. Focus of the Article: This study substantially exhibits the concepts of audience segmentation and targeting, informed by theory and formative research, and monitoring and evaluation research in the context of anti-smoking campaigns. Research Question: The article aims to answer how smoking behavior and message framing influence individuals’ counterarguing responses to anti-smoking advertisements and the implications for ad effectiveness. Approach: The research investigates the role of smoking behavior (smokers vs. non-smokers) and message framing (positive vs. negative) in shaping individuals’ counterarguing responses to anti-smoking advertisements, using two studies involving behavioral and neuroscientific methods. Methods: Study 1 employed an online video task with 1920 participants, examining the effects of smoking behavior and message framing on ad effectiveness. Study 2 used electroencephalography (EEG) to record brain activity from 31 participants while they viewed anti-smoking video advertisements, focusing on the neural correlates of counterarguing and their effect on anti-smoking ad effectiveness. Results: Study 1 found that smokers report lower ad effectiveness compared to non-smokers, and observed an interaction between smoking behavior and message framing. Study 2 found that smoking behavior significantly influenced brain activity in the dorsolateral prefrontal cortex (dlPFC), ventrolateral prefrontal cortex (VLPFC), and anterior cingulate cortex (ACC) regions in response to anti-smoking ads. Additionally, a significant interaction was observed between smoking behavior and ad framing on brain activity in all three regions. Recommendations for Research or Practice: The findings suggest that anti-smoking campaigns should be tailored to the specific needs and attitudes of smokers and non-smokers, with separate campaigns for each group. Future research could explore the role of other individual differences in shaping counterarguing responses, the long-term effects of exposure to different types of anti-smoking messages on smoking behavior, and using more advanced neuroimaging techniques to investigate the neural processes that underlie counterarguing responses to anti-smoking messages for better flow.

An integrated intelligent algorithm is proposed to optimize the reliability, maintainability, and total cost in the job shop production system. The algorithm consists of three basic modules of computer simulation. each comprising three phases of Algorithm, simulation, and Experiments/robustness validation. In the design phase, different scenarios are determined by changing parameters affecting the reliability, maintainability, and total cost. The job shop production system is simulated in the simulation phase. Then, a fuzzy simulation approach is implemented to run the simulation model for each scenario with ambiguous inputs. Accordingly, the investment cost, maintenance cost, mean time to repair (MTTR), and mean time to failure (MTTF) are obtained. Finally, the performance of different scenarios is assessed in the third module. ANN and DEA are separately used in this module and the preferred method is selected based on the robustness test and extensive sensitivity analysis. DEA and ANN are then employed to rank the design alternatives concerning the initial inputs and outputs. To show the applicability and superiority of the proposed integrated algorithm, it is applied to optimize the design of a fuzzy job shop production system consisting of five workstations.

Purpose: New business practices and the globalization of markets force firms to take innovation as the fundamental pillar of their competitive strategy. Research and Development (R&D) plays a vital role in innovation. As technology advances and product life cycles become shorter, firms rely on R&D as a strategy to invigorate innovation. R&D project portfolio selection is a complex and challenging task. Despite the management's efforts to implement the best project portfolio selection practices, many projects continue to fail or miss their target. The problem is that selecting R&D projects requires a deep understanding of strategic vision and technical capabilities. However, many decision-makers lack technological insight or strategic vision. This article aims to provide a method to capitalize on the expertise of R&D professionals to assist managers in making informed and effective decisions. It also provides a framework for aligning the portfolio of R&D projects with the organizational vision and mission.Design/methodology/approach: This article proposes a new strategic approach for R&D project portfolio selection using efficiency-uncertainty maps.Findings: The proposed strategy plane helps decision-makers align R&D project portfolios with their strategies to combine a strategic view and numerical analysis in this research. The proposed strategy plane consists of four areas: Exploitation Zone, Challenge Zone, Desperation Zone and Discretion Zone. Mapping the project into this strategic plane would help decision-makers align their project portfolio according to the corporate perspectives.Originality/value: The new approach combines the efficiency and uncertainty dimensions in portfolio selection into an integrated framework that: (i) provides a complete representation of the stochastic decision-making processes, (ii) models the endogenous uncertainty inherent in the project selection process and (iii) proposes a computationally practical and visually unique solution procedure for classifying desirable and undesirable R&D projects.