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This research (1) investigates value cocreation behavior and the underlying activities on social media; (2) examines the motivational values and psychological predictors of cocreation intention on social media via uses and gratification paradigm (U&G) and the Theory of Planned Behavior (TPB); (3) investigates an underlying mechanism linking the motivational values to cocreation intention, via attitude.

Structural Equation Modeling (SEM) and the mediation analysis are used to test the data from 417 Facebook users.

Cocreation intention is (1) robustly influenced by extrinsic motivational values, such as purposive value; (2) indirectly affected, through attitude, by intrinsic motivational values such as entertainment and social enhancement; and (3) not associated with subjective norms, behavioral control and self-discovery. The latter is a likely result of this study's focus on explaining value cocreation rather than social media usage.

This study is one of the first to identify antecedents of cocreation intention on Facebook, using an integrative model of TPB and U&G. Attitude serves as a key construct, mediating the effects of motivational values on cocreation intention, with mediation being partial for some values and complete for others. The existence of direct and indirect effects of motivational values and the mediating role of attitude points to the illustrious yet contested value–attitude–behavior hierarchy and offers explanations as to why some of the motivational values were not linked to the cocreation behavior on Facebook (Pelletier et al., 2020).

Firms should strive to influence the attitude of their users toward cocreation intention as it links the influence of motivational values on value cocreation. Managers should strive to prepare an appropriate platform where customers can easily interact with one another and communicate different value propositions. The goal should be to enable customers to derive extrinsic values as they interact with corporate-sponsored social media content. More specifically, purposive value, followed by socializing value, should be emphasized during social media content design. For example, the content should feature concrete and convenient informational and instrumental benefits (purposive value) and provide customers with tools that enable them to create social support, friendship and intimacy (socializing value). In addition, entertainment value should not be dismissed.

This research builds upon the emerging social media literature and a robust decision-making model to investigate value cocreation, predictors and an underlying mechanism explaining the relationships.

This paper aims to investigate the impact of brand equity (BE) on stock performance (i.e. stock return, volatility and beta), and compare the performance of a high brand equity stocks (HBES) portfolio with that of the overall market during market downturn, market upturn and total disturbance periods of the COVID-19 pandemic in 2020.

Stock performance data and brand valuation estimates are obtained from various sources to assemble a portfolio of HBES and conduct the analyses. Econometric models are estimated to examine the impact of BE on stock performance and compare the HBES portfolio performance versus the overall market.

BE was positively associated with stock return and negatively associated with both types of risk (volatility and beta) during the COVID-19 pandemic. Specifically, during the market downturn period, BE was positively related to stock return and negatively related to stock volatility; during the market upturn period, BE was negatively associated with both types of risk; and during the total disturbance period, BE was positively associated with stock return and negatively associated with both types of risk. Finally, the HBES portfolio outperformed the market (S&P 500 index).

The findings advance the extant research by providing evidence pertaining to brands' role in mitigating the impact of unpredictable market shocks and crises, such as the COVID-19 pandemic, on stock performance. While brands are mostly viewed as drivers of sustained competitive advantage and profitability, their protective role in crisis times is noteworthy.

The research findings potentially help marketing and brand managers to justify marketing spending and craft their strategies to enhance firm performance during crises similar to COVID-19.

The marketing–finance interface can benefit from insights offered by the COVID-19 pandemic, as such crises are becoming prevalent and are capable of damaging various stakeholders' outcomes (firms, investors and customers). The empirical examination is separately conducted on the market downturn, market upturn and total disturbance period attributable to the COVID-19 pandemic.

Nowadays, quality is one of the most important key success factors in the automobile industry. Improving the quality is based on optimizing the most important quality characteristics and usually launched by highly applied techniques such as failure mode and effect analysis (FMEA). According to the literature, however, traditional FMEA suffers from some limitations. Reviewing the literature, on one hand, shows that the fuzzy rule-base system, under the artificial intelligence category, is the most frequently applied method for solving the FMEA problems. On the other hand, the automobile industry, which highly takes advantages of traditional FMEA, has been deprived of benefits of fuzzy rule-based FMEA (fuzzy FMEA). Thus, the purpose of this paper is to apply fuzzy FMEA for quality improvement in the automobile industry.

Firstly, traditional FMEA has been implemented. Then by consulting with a six-member quality assurance team, fuzzy membership functions have been obtained for risk factors, i.e., occurrence (O), severity (S), and detection (D). The experts have also been consulted about constructing the fuzzy rule base. These evaluations have been performed to prioritize the most critical failure modes occurring during production of doors of a compact car, manufactured by a part-producing company in Iran.

Findings indicate that fuzzy FMEA not only solves problems of traditional FMEA, but also is highly in accordance with it, in terms of some priorities. According to results of fuzzy FMEA, failure modes E, pertaining to the sash of the rear right door, and H, related to the sash of the front the left door, have been ranked as the most and the least critical situations, respectively. The prioritized failures could be considered to facilitate future quality optimization.

This research provides quality engineers of the studied company with the chance of ranking their failure modes based on a fuzzy expert system.

This study utilizes the fuzzy logic approach to solve some major limitations of FMEA, an extensively applied method in the automobile industry.

This paper aims to present an analytical method, which combines the complex permeance (CP) and the superposition concept, to predict the air-gap magnetic field distribution in surface-mounted permanent-magnet (SMPM) machines with eccentric air-gap.

The superposition concept is used twice; first, to predict the magnetic field distribution in slot-less machine with eccentric air-gap, the machine is divided into a number of sections. Then, for each section, an equivalent air-gap length is determined, and the magnetic field distribution is predicted as a concentric machine model. The air-gap field in the slot-less machine with eccentricity can be combined from these concentric models. Second, the superposition concept is used to find the CP under eccentricity fault. At this end, the original machine is divided into a number of sections which may be different from the one for slot-less magnetic field prediction, and for each section, the CP is obtained by equivalent air-gap length of that section. Finally, the air-gap magnetic field distribution is predicted by multiplying the slot-less magnetic field distribution and the obtained CP.

The radial and tangential components of the air-gap magnetic flux density are obtained using the proposed method analytically. The finite element analysis is used to validate the proposed method results, showing good agreements with the analytical results.

This paper addresses the eccentricity fault impact upon the air-gap magnetic field distribution of SMPM machines. This is done by a combined analysis of the complex permeance (CP) method and the superposition concept. This contrasts to previous studies which have instead focused on the subdomain method.

Although Iran is one of the largest producers and exporters of saffron in the world, the organic saffron market in Iran is still in its early stages, and there is scarce empirical evidence in this regard. Therefore, the study's primary purpose is to segment the organic saffron market in Mashhad, Iran using neobehavioristic theory and machine learning methods.

Considering the neobehavioristic theory of consumer behavior, the organic saffron market was segmented using crisp and fuzzy clustering algorithms. Also, to assess the relative importance of the factors affecting the intention to buy organic saffron in each market segment, a sensitivity analysis was performed on the output of the artificial neural network (ANN). A total of 400 questionnaires were collected in Mashhad, Iran in January and February 2020.

In contrast to the belief that psychological factors are more important in market segmentation than demographic characteristics, findings showed that the demographic characteristics of consumers, especially education and income, are the dominant variables in the segmentation of the organic food market. Among the 4 A’s marketing mix elements, the results showed that a low level of awareness and accessibility are obstacles to organic saffron market development. Advertising, distribution channel improvement, package downsizing and online business development are suggested strategies for expanding the organic saffron market in Iran.

The results of the present study will help policymakers and suppliers of organic saffron to identify their target markets and design short- and long-term marketing strategies to develop the organic saffron market.

Machine learning methods and the neobehavioristic theory of consumer behavior were used to segment the organic food market.

To design a novel hybrid approach to illustrate a reciprocal alignment to integrate future products and technologies. This mixed qualitative-quantitative method aims to optimize the final product portfolio and production technologies alignment in the food industry.

A list of products and technologies is extracted and evaluated by experts employing Market Attractiveness and Ease of Implementations Matrix (MA-EI) for products and attractiveness and technological Capability Matrix (A-C) for technologies. Weights of high-scored alternatives are attained applying the Z-number extension of Best Worst Method (ZBWM). After the product-technology matrix is formed and the alignment scores of each pair are determined by experts. Subsequently, final scores are computed, and a framework is proposed by electing high-ranked products and technology of each cluster to form the aligned product and technology portfolios of a food and hygiene industry company.

By employing an uncertain multicriteria decision-making approach besides product and technology matrices in a food industry corporation, among 40 technology and product, 13 products by 6 technologies are proposed. Thus, only six technology are necessary to manufacture the highly important and effective products.

The combination of product and technology analysis matrixes with an uncertain decision-making approach is considered as a novel approach in this research. Moreover, the distinctness between the present study and other researches is the concurrent unified aspect of product portfolio and technology optimization and its implementation in the planning discussion, especially in the food industry.

Progressive collapse refers to a phenomenon, in which local damage in a primary structural component leads to total or partial structural system failure, without any proportionality between the initial and final damage. Robustness is a measure that demonstrates the strength of a structure to resist progressive collapse. Static pushdown and nonlinear dynamic analysis were two main procedures to calculate the capacity of structures to resist progressive collapse. According to previous works, static analysis would lead to inaccurate results. Meanwhile, capacity analysis by dynamic analysis needs several reruns and encountering numerical instability is inevitable. The purpose of this paper is to present the formulation of a solution procedure to determine robustness of steel moment resisting frames, using plastic limit analysis (PLA).

This formulation utilizes simplex optimization to solve the problem. Static pushdown and incremental dynamic methods are used for verification.

The results obtained from PLA have good agreement with incremental analysis results. While incremental dynamic analysis is a very demanding method, PLA can be utilized as an alternative method.

The formulation of progressive collapse resistance of steel moment frames by means of PLA is not proposed in previous research works.

The purpose of this paper is to present a 3D finite element model of the electromagnetic fields in an AC three‐phase electric arc furnace (EAF). The model includes the electrodes, arcs, and molten bath.

The electromagnetic field in terms of time in AC arc is also modeled, utilizing a 3D finite element method (3D FEM). The arc is supposed to be an electro‐thermal unit with electrical power as input and thermal power as output. The average Joule power, calculated during the transient electromagnetic analysis of the AC arc furnace, can be used as a thermal source for the thermal analysis of the inner part of furnace. Then, by attention to different mechanisms of heat transfer in the furnace (convection and radiation from arc to bath, radiation from arc to the inner part of furnace and radiation from the bath to the sidewall and roof panel of the furnace), the temperature distribution in different parts of the furnace is calculated. The thermal model consists of the roof and sidewall panels, electrodes, bath, refractory, and arc. The thermal problem is solved in the steady state for the furnace without slag and with different depths of slag.

Current density, voltage and magnetic field intensity in the arcs, molten bath and electrodes are predicted as a result of applying the three‐phase AC voltages to the EAF. The temperature distribution in different parts of the furnace is also evaluated as a result of the electromagnetic field analysis.

This paper considers an ideal condition for the AC arc. Non‐linearity of the arc during the melting, which leads to power quality disturbances, is not considered. In most prior researches on the electrical arc furnace, a non‐linear circuit model is usually used for calculation of power quality phenomena distributions. In this paper, the FEM is used instead of non‐linear circuits, and calculated voltage and current densities in the linear arc model. The FEM results directly depend on the physical properties considered for the arc.

Steady‐state arc shapes, based on the Bowman model, are used to calculate and evaluate the geometry of the arc in a real and practical three‐phase AC arc furnace. A new approach to modeling AC arcs is developed, assuming that the instantaneous geometry of the AC arc at any time is constant and is similar to the geometry of a DC arc with the root mean square value of the current waveform of the AC arc. A time‐stepping 3D FEM is utilized to calculate the electromagnetic field in the AC arc as a function of time.

Innovation has become an approach to create value for the customer to remain competitive in the market. However, previous research on innovation performance particularly among Bumiputera small and medium-sized enterprises (SMEs) had received little intention. Hence, Bumiputera SMEs need to inculcate the innovation culture to generate innovation performance. As such, the purpose of this study is to examine the ambidextrous orientation and innovation strategy on innovation culture, and how innovation culture could mediate the relationship between ambidextrous orientation and innovation strategy and innovation performance. In addition, this study also examines the role of government support as the moderator between innovation culture and innovation performance.

Data were collected from 140 Bumiputera SMEs and analyzed using partial least square-structural equation modeling via Smart PLS.

Findings indicated that ambidextrous orientation (alignment and adaptability) and innovation strategy (proactive creativity strategy and growth risk orientation strategy) had a significant impact on innovation culture. Besides, innovation culture mediated the relationship between alignment, proactive creativity strategy, growth risk orientation strategy and innovation performance. Surprisingly, innovation culture does not significantly mediate the relationship between adaptability and innovation performance. However, government support plays an important role to support innovation culture and innovation performance in Bumiputera SMEs.

This study makes both theoretical and practical contributions, especially in identifying the significant role of Bumiputera SMEs in creating an innovation culture. Besides, it explained government support as an important role in strengthening the relationship between innovation culture and innovation performance. The findings of the study will provide great help to Bumiputera entrepreneurs in formulating innovation culture in Malaysian SMEs.

Startups play a significant role in improving societies. This paper concentrates on the concept of “startups” and attempts to present a more comprehensive view of this phenomenon. Also, the focus of this paper is on investigating enterprising communities and the startup ecosystem in Iran.

The existing literature is carefully reviewed, definition and views are explored and the story of this phenomenon is told from the formation to the exit stage. The author elaborates the existing stages and challenges of startups in Iran. To do so, 65 tech startup founders are interviewed. Semi-structured interviews (SI) were based on previous studies mentioning the lifecycle of the startups, and were fully recorded. Moreover, secondary sources of data (SS) were used to support the findings.

The paper classifies the extant theories of startups in two levels, i.e. macro level, and micro and meso levels. Also, it defines some steps for the formation of startups and studies the Iranian startup ecosystem. Last but not the least the paper contributes to the startup ecosystem of Iran which is changing disruptively, especially in the last couple of years.

Findings of this research shed light on the existing ecosystem of startups in Iran, which is rarely studied in previous research. Lack of enough evidence in the existing literature of startup ecosystem in Iran was the most significant limitation of the research which increased the need for more investigation and elaboration.

The paper contributes to understanding the startup ecosystem in Iran, and the challenges.