کد مقاله : 202504231204730 بازدید : 44 صفحه: -

نوع مقاله: پژوهشی

Identification and Analysis of Influential Drivers for Future Consumer Behavior Analysis in the Development Stages of New Leather Products

محورهای موضوعی : Marketing

Meisam Masoumi ¹ , Alireza Rousta ² , ahmad askari ³

1 - Department of Business Management, Ki.C., Islamic Azad University, Kish , Iran
2 - Department of Business Management, ShQ.C., Islamic Azad University,Shahr-e Qods , Iran
3 - Department of Bussiness Management , Lam.C., Islamic Azad University , Lamerd, Iran

تاریخ دریافت : 1404/02/03 تاریخ پذیرش : 1404/05/06 تاریخ انتشار : 1404/07/25

کلید واژه: Driver, New Product Development, Consumer Behavior, Dorsa Leather.,

چکیده مقاله :

Background: The leather industry, regarded as one of the oldest and most dynamic sectors, has consistently experienced significant changes and transformations in consumer preferences, emerging technologies, and environmental regulations. A comprehensive understanding of the driving forces that influence consumer behavior analysis can significantly contribute to the development of innovative products and the refinement of marketing strategies within this sector.

Objective: The primary objective of this study is to identify and analyze the critical drivers affecting consumer behavior during the development stages of new leather products. By furnishing innovative insights grounded in empirical data, the research aims to optimize the product development processes within this industry and enhance responsiveness to the ever-evolving market demands.

Methodology: This study is an applied research endeavor employing a post-positivist paradigm, utilizing fuzzy Delphi methods for the screening of driving forces and the Marcus method for their prioritization. Data were collected through interviews with ten senior executives from the DarSa leather brand (employing purposive sampling) and a thorough literature review. In the initial phase, 22 driving forces were identified, which were subsequently screened using the fuzzy Delphi method, leading to the final selection of 9 key drivers.

Findings: The research findings reveal that the selected driving forces encompass shifts in consumer preferences, advancements in production technologies, environmental sustainability considerations, and brand social responsibility. These forces exert a profound and direct influence on the new product development processes in the leather industry, necessitating their incorporation into the strategic planning and development of new products to better align with market requirements.

Conclusion: This study effectively identifies and prioritizes the key drivers that impact consumer behavior and the development of new leather products. In light of the findings, it is recommended that leather manufacturers pay meticulous attention to market dynamics and fluctuations in consumer preferences, leveraging novel technologies as essential tools for fostering product innovation.

چکیده انگلیسی:

منابع و مأخذ:

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متن کامل:

Received: 23/04/2025

Accepted: 22/07/2025

$C:\Users\mkhakrangin\Desktop\iranban\cover_en.jpg$ Journal of System Management (JSM)

Online ISSN: 2783-4190, Print ISSN: 2783-428x

10.82134/sjsm.2026.1204730

12(2), 2026, pp. 75-86

RESEARCH ARTICLE Open Access

Identification and Analysis of Influential Drivers for Future Consumer Behavior Analysis in the Development Stages of New Leather Products

Meysam Masoumi¹, Alireza Rousta2*, Ahmad Askari3

Abstract

The leather industry, regarded as one of the oldest and most dynamic sectors, has consistently experienced significant changes and transformations in consumer preferences, emerging technologies, and environmental regulations. A comprehensive understanding of the driving forces that influence consumer behavior analysis can significantly contribute to the development of innovative products and the refinement of marketing strategies within this sector. The primary objective of this study is to identify and analyze the critical drivers affecting consumer behavior during the development stages of new leather products. By furnishing innovative insights grounded in empirical data, the research aims to optimize the product development processes within this industry and enhance responsiveness to the ever-evolving market demands.

This study is an applied research endeavor employing a post-positivist paradigm, utilizing fuzzy Delphi methods for the screening of driving forces and the Marcus method for their prioritization. Data were collected through interviews with ten senior executives from the DarSa leather brand (employing purposive sampling) and a thorough literature review. In the initial phase, 22 driving forces were identified, which were subsequently screened using the fuzzy Delphi method, leading to the final selection of 9 key drivers. The research findings reveal that the selected driving forces encompass shifts in consumer preferences, advancements in production technologies, environmental sustainability considerations, and brand social responsibility. These forces exert a profound and direct influence on the new product development processes in the leather industry, necessitating their incorporation into the strategic planning and development of new products to better align with market requirements.This study effectively identifies and prioritizes the key drivers that impact consumer behavior and the development of new leather products. In light of the findings, it is recommended that leather manufacturers pay meticulous attention to market dynamics and fluctuations in consumer preferences, leveraging novel technologies as essential tools for fostering product innovation.

Keywords: Driver, New Product Development, Consumer Behavior, Dorsa Leather

[1] 1. Department of Business Management, Ki.C., Islamic Azad University, Kish, Iran

2*. Department of Business Management, ShQ.C., Islamic Azad University, Shahr-e Qods , Iran (Corresponding Author: alirezarousta@iau.ac.ir)

3. Department of Bussiness Management , Lam. C., Islamic Azad University , Lamerd, Iran

Introduction

The leather industry, one of humanity’s oldest and most culturally significant sectors, has long served as a cornerstone of economic activity. Historically valued for its durability and aesthetic appeal in products like clothing, footwear, and accessories, the industry now navigates transformative challenges driven by evolving consumer preferences, technological advancements, and environmental imperatives (Ahmed & Rahman, 2021). Modern consumers increasingly prioritize sustainability, ethical transparency, and product quality—shifts that profoundly impact both product development and marketing strategies (Sharma & Gupta, 2022). Consequently, identifying key drivers of consumer behavior during new leather product development has become critical. Manufacturers must rigorously analyze these drivers—spanning environmental accountability, supply chain transparency, and market expectations—to design products aligned with dynamic consumer demands. Research in this domain not only clarifies behavioral influences but also equips producers with actionable insights for strategic innovation and distribution (Tanim et al., 2023).

advanced management systems, consumer behavior has undergone fundamental changes (Ghasemi & Rezaei, 2023). Manufacturers must utilize integrated data-driven management systems to analyze consumer behavior (Mohammadi et al., 2022).

Moreover, understanding consumer behavior and its drivers requires a deep analysis of relevant data and information. Numerous studies indicate that cultural, social, and economic factors significantly influence consumer choices. For this reason, producers need to employ advanced analytical tools to gather information on the preferences and behaviors of consumers and incorporate this data into their decision-making processes. Accurate recognition of these factors can assist manufacturers in developing products that align with market needs and expectations, thereby increasing the likelihood of competitiveness in this challenging industry. This issue underscores the importance of identifying and analyzing drivers affecting consumer behavior, as these factors not only influence product selection but can also serve as key components in creating effective strategies for marketing and developing new products. By understanding these drivers, researchers and producers can better maintain their competitive position in today's dynamic environment and respond to the growing market demand (Tanim et al., 2023).

Due to the increasing competition in the global market and rapid changes in consumer tastes and behaviors, the importance of gaining a profound understanding of the factors affecting consumers in the leather industry has risen significantly. Identifying influential drivers can help manufacturers develop new products that effectively address consumer needs and expectations. As the market confronts continuous changes, the ability to recognize and adapt to these changes can be the determining factor for a brand’s success or failure in the market. Therefore, research in this area is vital not only for producers but also for all industrial stakeholders, including designers, marketers, and researchers (Ahmed & Rahman, 2021). On the other hand, with rising environmental awareness and social responsibility, leather manufacturers must pay special attention to the new expectations consumers have regarding the sustainability and ethical nature of their products. This awareness becomes particularly significant concerning leather, recognized as a product with environmental impacts. Research indicates that consumers increasingly trust brands committed to sustainability in their production and sourcing practices. Hence, failing to recognize the influential drivers in this context can lead to diminished brand credibility and a decrease in market share (Sharma & Gupta, 2022). Moreover, research on the effective drivers of consumer behavior can enable manufacturers to develop appropriate, data-driven marketing strategies. In today’s world, where data is recognized as the most valuable information resource, utilizing these data to better understand consumer behavior and forecast future needs is essential. Such analyses can lead to innovation and create a foundation for developing new products, which directly affects marketing strategies and product delivery, ultimately resulting in increased sales and customer satisfaction. In conclusion, this research can serve as a scientific basis for a better understanding of market trends and changes in the leather industry. Given the rapid pace of change in today's world, offering data-driven solutions and conducting thorough analyses of information can help producers remain competitive in the market and capitalize on innovative opportunities. This research will not only enhance the competitiveness of the leather industry but can also serve as a valuable resource for developing new methods and solutions in the design and production processes (Tanim et al., 2023). Finally, the primary question of this research addresses the effective drivers of consumer behavior in the stages of new leather product development.

Literature Review

New product development (NPD), is the process of designing, producing, and launching a new good or service into the market, aimed at addressing the needs and desires of consumers. This process encompasses various stages, including market research, product design, prototype development, testing, marketing, and ultimately, market introduction. The primary objective of NPD is to create added value and enhance competitive advantage within the market (Cooper, 2019). NPD can have significant impacts on the leather industry. With advancements in technology and an increasing demand for sustainable and environmentally friendly products, leather companies are striving to adopt innovative technologies and offer products that excel both in quality and environmental sustainability. For instance, the use of recycled leathers and low-consumption methods for production not only aids in cost reduction but also enhances consumer satisfaction. Moreover, the introduction of new products can create market differentiation and attract new customers (Giuntini & Tani, 2021).

The following summarizes relevant research in this field:

Farjami and Rafiei (2022), examined the factors influencing consumer buying behavior in Iran's leather industry. This study investigated the cultural, social, and economic factors affecting consumer purchasing behavior and analyzed their impact on consumer decision-making. Hosseini and Zarin (2021), focused on the effects of product innovations on consumer behavior in the leather sector. Their research indicates that innovations can facilitate improvements in consumer purchasing behavior. Youssefi and Alizadeh (2023), explored the role of sustainability in purchasing behavior regarding leather products, analyzing the influence of environmental requirements and sustainability on consumer buying patterns in the leather industry. Soltani and Hosseini (2022), analyzed the preferences of consumers inclined toward sustainable leather products, identifying purchasing patterns through consumer taste data. Mahmoudi and Vafaie (2021), investigated the impact of digital advertising on consumer behavior in the Iranian leather market, demonstrating that digital marketing plays a significant role in enhancing sales.

Ahmed and Rahman (2021), studied consumer preferences and sustainability in leather products, emphasizing the implications for product development. Sharma and Gupta (2022), examined the impact of environmental concerns on consumer behavior within the leather industry, asserting the need for environmental awareness to be incorporated into marketing strategies. Tanim et al. (2023), focused on innovation in the leather industry, exploring consumer behavior and market dynamics, providing insights for producers to better align with market needs. Purohit and Das (2022), analyzed consumer behavior in the luxury leather market, investigating the influence of brand loyalty on purchasing decisions. Finally, Kaur and Singh (2021), contributed by understanding consumer attitudes toward sustainable leather products, revealing that positive environmental attitudes can significantly influence consumer purchasing behavior.

Research Methodology

The present study was designed with a post-positivist approach, primarily aiming to be exploratory and applicable. In terms of data collection, this research is categorized as a field study and employs a mixed-methods methodology. For data analysis, both fuzzy Delphi and Marcus methods were utilized, both of which are fundamentally reliant on quantitative data. The statistical population of this research consists of brand managers from the leather company Dorsa, and sampling was conducted using a judgmental approach. To ensure the accuracy and quality of the information, the selection of samples was based on the expertise and experience of the managers in the field of marketing management, and the sample size was limited to 14 individuals. The data collection tools in this study included semi-structured interviews and standardized questionnaires. The driving factors of the research were identified through a literature review and interviews with experts. Subsequently, a questionnaire was utilized for expert screening of these factors, and a priority-ranking questionnaire was employed to rank them.

The research consists of the following stages:

1. Literature Review and Expert Interviews:

This stage aims to identify the key drivers influencing consumer behavior analysis in new leather product development.

2. Driver Filtering:

The fuzzy Delphi method is used here to screen and refine criteria and influencing factors.

3. Driver Prioritization:

The MARCOS method (Measurement of Alternatives and Ranking according to COmpromise Solution) ranks the drivers to determine their importance and impact.

Details on the Fuzzy Delphi Stage (Ahmadi et al., 2023):

To implement the fuzzy Delphi method:

- Expert opinions are collected and fuzzified (converted into fuzzy values).

- A credible fuzzy scale translates qualitative (verbal) judgments into numerical data.

- This study uses a 5-point Likert scale (Table 1) for this conversion.

- This approach enhances the accuracy and validity of findings, enabling more precise analysis of consumer behavior drivers in the leather industry.

Table 1.

Fuzzy Triangular Numbers in Fuzzy Delphi Method

Step 2: Fuzzy Integration of Fuzzified Values. After selecting the appropriate fuzzy scale, expert opinions are collected and transformed into fuzzified values. Various methods are utilized for integrating the fuzzy views of the experts. If each expert's opinion is represented as triangular fuzzy numbers denoted by (l, m, u), the simplest approach to calculate the fuzzy mean of the opinions is to employ the fuzzy average formula, which separately addresses the minimum, average, and maximum values of these numbers:

Step 3: Defuzzification of Data. In fuzzy methods, the researcher ultimately converts the final fuzzy values into crisp numbers. Various tools are available for defuzzification, one of which is the fuzzy triangular number average. This tool represents the simplest method for obtaining a specific numerical value from fuzzy numbers and can facilitate subsequent analyses.

Step 4: Threshold Determination. After selecting an appropriate method for defuzzification, the researcher must establish a specific threshold value. This threshold may vary depending on the researcher’s perspective in different studies. If the crisp value resulting from defuzzification for any driver exceeds the established threshold, that driver remains in the analysis; otherwise, it is excluded from the research process.

Marcus Method: The Marcus method is a modern multi-criteria decision-making technique employed to rank and evaluate options based on optimal solutions. The inputs for this method include the following:

1. Decision matrix;

2. Weights of the criteria;

3. Characteristics of the criteria (positive or negative).

Steps of the Marcus Method:

- Step 1: Forming the Decision Matrix. In all multi-criteria decision-making techniques aimed at ranking, it is essential to establish a decision matrix. In the Marcus technique, two criteria are considered for evaluating (n) options. Each option is assigned a score based on each criterion, which can be determined either from actual quantitative values or theoretically through qualitative assessments. Consequently, a decision matrix of size (m*n) is prepared for this process.

- Step 2: Determining Ideal and Anti-Ideal Points. In this stage, the values related to the ideal (AI) and anti-ideal (AAI) points are specified. The variable (B) represents criteria oriented towards benefits, while (C) is used for cost-oriented criteria.

- Step 3: Normalization. This stage involves normalizing the criteria based on cost and benefit perspectives. The output of this process is a matrix that aligns all criteria to a benefit (positive) nature; this is because the chosen normalization method is linear.

- Step 4: Weight Assignment. In this section, the weights of the criteria in the normalized matrix are multiplied to create a new weighted matrix.

- Step 5: Determining the Degree of Preference for Options. In this stage, the ideal (K+) and anti-ideal (K-) degrees of preference for the options are calculated.

- Step 6: Final Performance Determination and Ranking of Options. In this step, the optimal performance of each option is identified. Thus, f(k-) refers to the performance of the anti-ideal point, while f(k+) refers to the performance of the ideal point for each option. Additionally, based on the values obtained from f(k), the options are ranked; any option with a higher f(k) value will receive a higher rank.

These steps can significantly enhance the accuracy and quality of the result analysis in research related to the leather industry.

Research Findings

Initial identification of drivers influencing consumer behavior during new leather product development was conducted through a comprehensive literature review and specialized interviews with industry experts. This process yielded 22 distinct drivers. Among these, three drivers emerged exclusively from expert interviews:

1. Socio-Cultural factors,

2. Technological advancements, and

3. Innovation.

The remaining 19 drivers were derived solely from existing literature, encompassing domains such as economic conditions, competitive dynamics, product pricing, and brand perception. Table 2 provides the complete list of drivers and their respective sources.

Table 2.

List of Research Drivers

Verbal Variable	Fuzzy Value	Fuzzy Triangular Number
Very Low		(25/0 ,0 ,0)
Low		(5/0 ,25/0 ,0)
Medium		(75/0 , 5/0 ,25/0)
High		(1 , 75/0 , 5/0)
Very High		(1 ,1 , 75/0)

Source	Driver
Purohit & Das (2022) Ghosh & Roy (2021)	Economic
Interview	Socio-Cultural
Interview	Technological
Kaur & Singh (2022) Aaker (2022)	Competitive
Chen & Zhao (2023) Elghandour & Ibrahim (2021)	Targeting
Interview	Innovation
Mahdavi & Nasir (2022) Kotler & Keller (2021)	Marketing
Ali & Kim (2023) Dutta (2023)	Management
Zhang & Jiang (2023)	Environmental
Yousaf & Ali (2023) Mohiuddin & Rahman (2021)	Regulations and Laws
Lee & Choi (2022) De Silva & Ratnayake (2021)	Organizational Culture
Paliy & Vendina (2022) Ellram & Liu (2023)	Infrastructure
Faith & Rahim (2022) Mohammadi & Asgarian (2021)	Product Quality
Chen & Wang (2022) Saleh & Enany (2021)	Product Pricing
Aaker (2022) Huang & Sarigöllü (2021)	Brand
Lee & Choi (2022) Matzler & Hinterhuber (2021)	Customer Satisfaction
Ali & Kim (2023) Lemon & Verhoef (2022)	Experience
Sun & Zhang (2022) Kolsaker & Ghadiri (2021)	Sales
Dutta (2023) Kahn & McAllister (2021)	Brand Image
Ramayah & Gilani (2022)	Competitiveness
Kahn & Tversky (2022) Swaminathan & Sinha (2021)	Information
Yazdani & Younesi (2021) Karpova & Choi (2022)	Culture

The identified drivers were subsequently screened through the distribution of expert judgment questionnaires and the implementation of the fuzzy Delphi method. In this study, a threshold value of 0/7 was established for the final evaluation. Drivers with a defuzzified value exceeding 0/7 were selected for the final prioritization process using the Marcus method. According to the obtained results, 9 drivers were identified as options with defuzzified values higher than 0/7 and were thus considered for final prioritization. Table 3 presents the output from the fuzzy Delphi analysis for the final drivers.

Table 3.

Fuzzy Delphi Output for Final Drivers

Defuzzified Value of Each Driver	Experts' Average Opinions			Research Drivers
Defuzzified Value of Each Driver	Upper Bound	Median	Lower Bound	Research Drivers
87/0	95/0	87/0	79/0	Product Quality (A)
78/0	89/0	77/0	68/0	Brand (B)
75/0	85/0	73/0	67/0	Product Pricing (C)
79/0	91/0	79/0	67/0	Customer Satisfaction (D)
83/0	92/0	81/0	76/0	Socio-Cultural (E)
77/0	85/0	77/0	69/0	Marketing (F)
74/0	81/0	74/0	67/0	Technology (G)
81/0	90/0	82/0	71/0	Innovation (H)
76/0	83/0	76/0	69/0	Competitiveness (I)

Subsequently, the selected drivers will be prioritized using the Marcus decision-making method. The Marcus method is an innovative technique in multi-criteria decision-making designed based on a decision matrix. In this stage, 14 knowledgeable experts provided their views on each driver according to three indices: expert expertise, intensity of importance, and certainty, using a scale of 10. Among these indices, the expert expertise and intensity of importance possess a positive nature, while the certainty index has a negative aspect. These indices are derived from the global business network approach, which is commonly employed in foresight studies. Table 4 illustrates the arithmetic mean of the experts' opinions regarding each of the drivers.

Table 4.

Decision Matrix

Decision Matrix	Expert Expertise (+)	Intensity of Importance (+)	Certainty (-)
A	43/9	29/9	56/2
B	34/8	51/8	82/4
C	87/6	12/7	42/6
D	71/8	57/8	12/4
E	23/9	18/9	27/3
F	75/7	87/7	23/5
G	29/6	18/6	11/7
H	04/9	18/9	68/3
I	19/7	01/7	69/5
Ideal Option	43/9	29/9	56/2
Anti-Ideal Option	29/6	18/6	68/3

In the next step, the data contained in the decision matrix are processed using linear normalization. In this method, the positive indices are divided by the maximum value in their respective columns, while the negative indices are divided by the minimum value in each column. Subsequently, these normalized indices are combined with the data from the normalized matrix.

Table 5.

Normalized Matrix

Normalized Matrix	Expert Expertise	Intensity of Importance	Certainty
A	1	1	1
B	884/0	916/0	531/0
C	729/0	766/0	399/0
D	924/0	922/0	621/0
E	979/0	988/0	783/0
F	822/0	847/0	489/0
G	667/0	665/0	360/0
H	959/0	988/0	696/0
I	762/0	755/0	450/0
Ideal Option	1	1	1
Anti-Ideal Option	667/0	665/0	696/0

In this study, the expert weights for each of the three indices are considered equally, each being set at 0.33. Therefore, after multiplying the indices by the data from the normalized matrix, a weighted normalized matrix is produced.

Table 6.

Weighted Normalized Matrix

Weighted Normalized Matrix	Expert Expertise	Intensity of Importance	Certainty
A	33/0	33/0	33/0
B	292/0	302/0	175/0
C	241/0	253/0	132/0
D	305/0	304/0	205/0
E	323/0	326/0	258/0
F	271/0	280/0	161/0
G	220/0	219/0	119/0
H	316/0	326/0	230/0
I	251/0	249/0	149/0
Ideal Option	33/0	33/0	33/0
Anti-Ideal Option	220/0	219/0	230/0

Based on the data from the weighted normalized matrix, the performance indicators of the Marcus method will be calculated. Table 7 presents these performance indicators for each of the research drivers. The results indicate that the drivers of product quality, socio-cultural factors, and innovation hold a higher priority.

Table 7.

Marcus Performance Indicators

Marcus Performance Indicators	Ki-	ki+	f(ki-)	f(ki+)	Final Score of Each Driver
A	791/1	0/874	327954/0	672045/0	753/0
B	085/1	529/0	327757/0	672243/0	456/0
C	050/2	1	327868/0	672131/0	862/0
D	464/1	714/0	327823/0	672176/0	616/0
E	180/1	576/0	328018/0	671982/0	497/0
F	681/1	820/0	327868/0	672131/0	707/0
G	037/1	506/0	327932/0	672067/0	436/0
H	1/255	612/0	327798/0	672201/0	528/0
I	569/1	766/0	328051/0	671949/0	660/0

Discussion and Conclusion

This study aimed to identify and prioritize drivers influencing consumer behavior during new leather product development. Through literature review and industry expert interviews, 22 initial drivers were extracted. Screening via the Fuzzy Delphi method (defuzzification threshold ≥0/7) yielded 9 key drivers. Final prioritization using the MARCOS technique revealed Product Quality, Socio-Cultural Factors, and Innovation as the most critical drivers, respectively.

Product Quality – the primary driver – directly impacts customer satisfaction and brand differentiation. In competitive markets, tangible quality (e.g., material durability, craftsmanship) not only fosters customer loyalty but creates added value. Socio-Cultural Factors, particularly environmental responsibility and ethical production, increasingly determine product selection. Innovation through sustainable technologies (e.g., plant-based leather) and design enhancements plays a vital role in capturing new markets. Secondary drivers (pricing, technology, competitiveness) dynamically interact with core drivers:

- Customer satisfaction (resulting from quality/innovation) drives loyalty and word-of-mouth;
- Emerging technologies enable operationalization of innovation and social responsibility.

Ultimately, success in leather product development requires integrating three pillars: objective quality, socio-cultural responsiveness, and systematic innovation.
Research Limitations:

Four key limitations warrant acknowledgment:

1. Geographical Constraint: Sampling focused solely on Iranian leather industry managers (Dorsa Leather), limiting generalizability to global markets (e.g., EU/North America).

2. Temporal Bias: Data collection (2023–2024) may not capture rapid market shifts (e.g., bio-technological advancements, new environmental regulations).

3. Stakeholder Homogeneity: Excluding end-consumers from driver prioritization risks overlooking emerging preferences (e.g., Gen Z’s circular economy expectations).

4. Subjectivity in MCDM Methods: Despite methodological rigor, Fuzzy Delphi and MARCOS outcomes remain contingent on expert judgments.

Suggestions for Future Research

To address these limitations, four research pathways are proposed:

1. Cross-Cultural Validation: Conduct comparative studies in diverse markets (e.g., Italy, India, China) to examine socioeconomic impacts on driver prioritization.
2. Hybrid Methodologies: Integrate quantitative surveys with qualitative social media analytics (e.g., NLP of user reviews) to mitigate stakeholder bias.

3. Longitudinal Tracking: Monitor 5-year driver evolution under emerging influences (e.g., lab-grown leather, EU Green Deal policies).

4. Large-Scale Behavioral Data: Leverage CRM datasets to empirically analyze relationships between drivers and actual purchasing patterns.

Despite limitations, this research provides a strategic framework for aligning leather product development with market expectations. Implementing these future directions will advance understanding of industry dynamics.

Acknowledgments

Since this research stems from a doctoral thesis in Public Management, with a focus on Human Resource Management at the Faculty of Humanities, Islamic Azad University of Kish, I would like to express my utmost gratitude to my supervising professors and dissertation advisor.

Conflict of Interest

There is no conflict of interest in conducting this study.

Author Contributions

All authors contributed equally to the writing of this article.

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آدرس مقاله

Identification and Analysis of Influential Drivers for Future Consumer Behavior Analysis in the Development Stages of New Leather Products

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