A novel extension of Pythagorean fuzzy MULTIMOORA approach for new product development

Enhancing readiness degree for Industrial Internet of Things adoption in manufacturing enterprises: An integrated Pythagorean fuzzy approach

This study proposes a hybrid multi-criteria decision-making (MCDM) methodology designed to enhance the readiness for adopting the Industrial Internet of Things (IIoT) in manufacturing enterprises. The Pythagorean fuzzy approach is employed to address uncertainty and imprecision throughout decision-making processes. The development framework in this study incorporates TOE (Technology-Organization-Environment) and HOT fit (Human-Organization-Technology) to pinpoint barriers to IIoT adoption. Additionally, a triple helix model (THM) emphasizing on the synergy among university-industry-government is utilized to formulate pragmatic strategies. The agro-food processing industry in Thailand is used as a case study. In this study, even barriers are identified and validated through the Delphi method. The SWARA (Step-wise Weight Assessment Ratio Analysis) method determines the importance weights of these barriers, revealing "lack of digital culture", "lack of knowledge and expertise," and "job displacement concerns" as the three most critical barriers. The COBRA (COmprehensive Distance Based Ranking) method is employed to prioritize pragmatic strategies under THM, indicating that the role of the university in enhancing human capital capabilities is the most important, followed by the government's roles in enabling national ICT infrastructures and offering investment incentives as the second and third pragmatic strategies, respectively. A sensitivity analysis validates the proposed framework's reliability and robustness. The study's findings emphasize the potential of this integrated framework to guide future research endeavors among scholars and academicians across diverse industries beyond agri-food processing.

Effects of physical activity and self-control on mobile phone addiction in college students: a cross-lagged study in China

Purpose

This study aims to investigate the impact of physical activity and self-control on college students' mobile phone addiction through cross-lagged longitudinal surveys, addressing the limitations of previous cross-sectional studies.

Patients and methods

A total of 414 college students were tracked three times during a 12-month period using the Physical Activity Rating Scale-3 (PARS-3), the Mobile Phone Addiction Tendency Scale (MPATS), and the Brief Self-Control Scale (BSCS). AMOS25.0 software was used to construct the cross-lagged relationship model, and the maximum likelihood approach was employed to investigate the model fitting. The asynchronous correlation between variables was investigated from the time series through the cross-lagged path coefficient.

Results

The fitting indexes of the cross-lagged model showed x 2/df = 5.098, GFI = 0.977, NFI = 0.969, IFI = 0.975, CFI = 0.974; RMSEA = 0.100, and SRMR = 0.030. The calculation conducted by combining the path coefficient of the model shows that PA and SC are the antecedent variables of MPA, and PA is the antecedent variable of SC. In addition, SC serves as a mediator in the path of PA, affecting MPA.

Conclusion

(1) physical activity can positively affect subsequent self-control; (2) physical activity can negatively influence subsequent mobile phone addiction; (3) self-control can negatively affect subsequent mobile phone addiction; and (4) physical activity can indirectly influence subsequent mobile phone addiction through self-control.

A historical review and analysis on MOORA and its fuzzy extensions for different applications

Multi-criteria decision-making (MCDM) methods have been widely used among researchers to provide a trade-off solution between best and worst, considering conflicting criteria and sets of preferences. An efficient and systematic literature review of these methods is needed to maintain their application in distinctive domains. To this end, this paper presents a comprehensive and systematic literature survey on "multi-objective optimization by ratio analysis" (MOORA) method and its fuzzy extensions developed and discussed in recent years. This review includes articles categorized based on the publication name, publishing year, journal name, type of applications, and type of fuzzy extensions. In addition, this review will enhance the understanding of practitioners and decision-makers on the MOORA method, its development, fuzzy hybridization, different application areas, and future work. The study revealed that the MOORA technique was predominantly used with the TOPSIS approach, followed by the AHP and COPRAS methods. Furthermore, 76.28 % use single and hybridization approaches among all MOORA studies, while 23.72 % use MOORA in a fuzzy environment.

Pythagorean fuzzy transportation problem: New way of ranking for Pythagorean fuzzy sets and mean square approach

The predominant domain for optimization in the current situation is the transportation problem (TP). In the majority of cases, accurate data have been employed, yet in reality, the values are vague and imprecise. In any decision-making process, imprecision is a significant issue. To deal with the ambiguous setting of collective decision-making, many tools and methods have been established. The Pythagorean fuzzy set is an extension of fuzzy sets that successfully handles ambiguity and fuzziness. To overcome the shortcomings of intuitionistic fuzzy context, Pythagorean fuzzy sets are considered to be the most recent tools. This study proposes a new method for addressing the uncertain Pythagorean transportation issue. In this study, we created a novel sorting technique for Pythagorean fuzzy sets that converts uncertain quantities into crisp numbers. We developed an innovative mean square strategy for obtaining the initial basic feasible solution (IBFS) for a Pythagorean Fuzzy Transit Issue (PyFTP) of three types (I, II, III) wherein the requirement, availability, and unit of transportation expenses are all in Pythagorean uncertainty. In addition, we used the MODI technique to find the best option. To demonstrate the suggested strategy, we used numerical problems of three distinct kinds. A comparison table with the results of the previous strategy and the suggested method is created to state the benefits of the ranking methodology with the proposed algorithm. The discussion of future research and conclusions is the final step.