Identification and Prioritization of Digital Content Marketing Factors for Brand Equity in the Leather Industry

Introduction

Digital communication gives leather businesses opportunities to explain product characteristics, communicate brand identity, and maintain customer relationships beyond individual transactions. It also exposes brands to public evaluation through reviews, comments, and shared experiences. For these businesses, the practical question is how to organize content marketing around customer needs while maintaining the resources and consistency required for sustained implementation.

Brand equity provides a conceptual basis for examining this question. Customer-based brand equity concerns the difference that brand knowledge makes to consumers’ responses to marketing. Brand awareness and the associations held in memory therefore matter alongside the information communicated in any individual message (Keller, 1993). Content marketing may contribute to these processes, but publishing content is not itself evidence that brand equity has improved.

In the leather industry, product authenticity, materials, durability, care, environmental claims, and animal-welfare concerns are potential topics for brand communication. These topics inform content design but were not measured as outcomes in this study. The expert exercise evaluates perceived marketing priorities, not the environmental performance of leather products or their alternatives.

Digital marketing capability involves more than access to a website or a social media account. Firms must plan, coordinate, and evaluate their activities. Masrianto et al. (2022) examined digital marketing capability in 217 Indonesian companies and highlighted organizational readiness, managerial innovativeness, and perceived usefulness. Olson et al. (2021) similarly considered how different business strategies influence the selection of digital marketing priorities and tactics. These studies support examining organizational conditions alongside customer-facing activities.

Digital content marketing is also broader than promotional messaging. Terho et al. (2022) connected customer-oriented content activities with the customer journey, drawing on interviews with managers in business markets. The distinction is relevant to leather firms: educational material, product-care information, and responses to customer questions may serve different purposes from direct advertising. Treating all such activities as interchangeable can obscure the capabilities required to perform them.

The present study addresses the following question: Which factors do experts consider relevant to digital content marketing intended to support brand equity in the leather industry, and how are those factors prioritized? Its contribution is an exploratory organization of candidate factors into an industry-oriented framework. The design does not estimate causal effects of marketing activities on customers’ brand perceptions or business performance.

Literature review

Customer interaction and digital communication

Research in hospitality illustrates the importance of customer-generated information. Dimitrios et al. (2023) reviewed digital marketing strategies in luxury hotels and emphasized social media reviews. Keke (2022) examined digital marketing and social media in the Turkish business context. Although neither study concerns leather products directly, both provide context for examining the relationship between a firm’s messages and consumers’ responses. Their conclusions should not be treated as direct validation of the present industry-specific framework.

The literature also connects digital communication to broader growth processes. Rizvanović et al. (2023) developed a framework linking digital marketing with start-up growth drivers, including customer interaction and business development. Mathews et al. (2016), using data from 224 Australian firms, examined internet marketing capabilities and international market growth. Bianchi and Mathews (2016) studied 204 Chilean exporters and linked internet marketing capabilities with information availability, business networks, and export growth. These studies support attention to capabilities and relationships, while addressing outcomes distinct from customer-based brand equity.

Organizational resources and implementation

Digital marketing practices depend on the context in which firms operate. Setkute and Dibb (2022) identified barriers in small business-to-business firms, showing why approaches developed for larger firms or consumer markets may not transfer directly. Hagen et al. (2022) examined the role of resources in digital marketing activities among Dutch place management partnerships. Melović et al. (2020) investigated digital transformation, digital marketing, and brand-related business activities in Montenegro. Together, these studies provide a rationale for considering employee support, organizational readiness, planning, and resources within a content marketing framework.

Faruk et al. (2021) analyzed 925 digital marketing publications from 2000 to 2019. Their bibliometric findings placed strategic planning, mobile marketing, and customer characteristics among the field’s prominent themes. This breadth helps explain why a framework limited to content quality or advertising channels would omit relevant organizational and audience-related considerations.

Research focus

The reviewed studies address different industries, units of analysis, and outcomes. They do not establish a universal ranking for leather-industry content marketing. The present investigation therefore brings literature-derived indicators together for expert assessment, retaining a distinction between external factors and internal organizational conditions. This is a framework-development exercise rather than a systematic review or a test of a fully specified causal model.

Methods

Study design

The study used an applied exploratory design with three successive Delphi rounds. Initial indicators were drawn from theoretical discussions and previous studies, then evaluated and refined by experts. Item generation was based on a narrative literature review; a reproducible systematic search protocol was not available.

Expert selection and study setting

Experts were selected purposively for knowledge or experience in digital marketing, content marketing, branding, brand equity, or the leather industry. Most item-level distributions contain 10 responses. Two distributions contain 11 responses, and one is incomplete; a constant panel size cannot therefore be established from the aggregate data. Recruitment dates, geographical coverage, eligibility thresholds, professional profiles, invitation counts, and participant retention across rounds were not recorded in the available study documentation.

Instrument and Delphi procedure

The initial questionnaire comprised 52 indicators across nine components. Experts rated importance on a five-point scale ranging from very low to very high. They could also suggest revisions or deletion. In round one, the initial indicators were assessed; frequencies and reported weighted scores were then summarized. Round-one results and the revised indicators were returned for reassessment in round two, and the process was repeated in round three.

Three indicators were removed after round one: preference for traditional processes, participation prompted by content addressing higher-order needs, and environmental pressure. The remaining 49 indicators appear in the later-round tables. The source describes these deletions as based on expert judgment about suitability. It does not provide an a priori numerical retention threshold, a formal consensus definition, or a stopping rule. Consequently, retention is reported without asserting that every item met a validated consensus criterion. Explicit consensus definitions are essential to transparent Delphi reporting (Diamond et al., 2014).

Descriptive analysis and numerical checks

For this report, descriptive means were calculated directly from the recorded aggregate frequencies, assigning scores of 1 to 5 to the five ordered importance categories. The calculation was M = Σ(k × fₖ) / Σfₖ, where fₖ is the recorded frequency for score k. Each complete distribution used its own recorded total as the denominator. These calculations provide internally consistent summaries of the available frequencies; they do not verify the accuracy of the underlying response records.

Tables 1–9 present the recorded response distributions and recalculated means. Frequency dashes in the underlying tables were coded as zero, whereas an empty cell was retained as missing. No response frequency was imputed or altered. For the incomplete SMS-advertising distribution in round two, a mean was not calculated. The two distributions totaling 11 responses are identified in Table 5. The original qualitative consensus labels were excluded because they were not derived from a defined agreement statistic. Importance means are not interpreted as evidence of consensus.

Each round cell presents frequencies for importance scores 1, 2, 3, 4, and 5, followed by the mean recalculated from that distribution. Frequency dashes in the underlying records are displayed as zero. NA denotes a missing frequency or a mean that could not be calculated. An em dash denotes an item not reassessed after round one. An asterisk identifies a distribution totaling 11 responses; all other complete distributions total 10. Values summarize perceived importance and are not consensus statistics.

Hierarchical priority weights and sensitivity output

The source includes a hierarchical model and software-generated synthesis and sensitivity displays. The synthesis output reports an overall inconsistency value of 0.08, a global priority of 0.110 for customer satisfaction, and domain weights of 0.702 and 0.298. These outputs suggest a separate hierarchical weighting procedure, but the manuscript does not document the pairwise-comparison questions, judgment scale, matrices, expert-aggregation procedure, software version, or calculation of the inconsistency measure. The weights are therefore reproduced as reported outputs and are not equated with the Delphi means.

The available sensitivity screenshot does not specify the perturbations, parameter ranges, or rank-reversal thresholds required to evaluate robustness. No new sensitivity result is claimed. Figure 1 reproduces the ten highest displayed weights using English labels; it is a redrawing of the supplied numerical output, not a recalculation from unavailable matrices.

Results

Structure and retention of indicators

The framework contains two domains and nine components. The external domain comprises consumer characteristics, cultural readiness, and customer participation. The organizational domain comprises brand personality, organizational culture, content production strategies, strategic planning, content creation, and advertising. Of 52 initial indicators, 49 were retained after round one. The three exclusions are shown in Tables 2, 3, and 5. The framework describes factors considered relevant by experts; it does not establish that the domains are statistically independent or that each indicator measures a distinct construct.

External factors

Within consumer characteristics, customer satisfaction had the highest final-round mean (4.90), followed by customer motivation (4.80), customer involvement (4.70), and perceived enjoyment (4.60). The recalculated final mean for customer insight was 4.50 (Table 1).

Within cultural readiness, willingness to innovate had the highest final mean (4.80), followed by trust in digital marketing tools (4.70). The final mean for the company’s ability to gain customer trust was 3.50 (Table 2).

The item with no round-two or round-three result was removed after round one.

For customer participation, using feedback and comments to improve content quality had the highest reported final mean of 4.70. Content aligned with basic information needs and communication needs had means of 4.40 and 4.30, respectively. Content aligned with higher-order needs was removed after round one. These results concern perceived importance; they do not measure actual posting, commenting, or other customer behavior. Table 3 presents the response distributions across rounds.

The item with no round-two or round-three result was removed after round one.

Organizational factors

Useful articles about products, the company, or related subjects had the highest final mean within brand personality (4.50; Table 4).

Employee support for change led organizational culture (4.80). Openness to suggestions had a recalculated mean of 4.09 in rounds two and three, based on 11 recorded responses per round. These two distributions differ in total response count from the other complete distributions (Table 5).

The item with no round-two or round-three result was removed after round one.

Within content production strategies, low cost had the highest final mean (4.80), followed by appropriate delivery (4.60) and content marketing design and planning (4.50; Table 6).

Identifying content marketing objectives led strategic planning (4.70), followed by developing target audience personas (4.50; Table 7).

Content structure led content creation (4.60), followed by content quality (4.50); brief content had a recalculated final mean of 3.60. Content distribution and production/distribution techniques were retained despite low final means of 1.50 and 1.80, respectively (Table 8).

Social media advertising led the advertising component (4.30). The second-round SMS-advertising mean was not calculated because one frequency was missing (Table 9).

Reported global prioritization

The source weighting output ranked customer satisfaction first at 0.110, willingness to innovate second at 0.094, and customer motivation third at 0.061. External factors received a reported weight of 70.2% and organizational factors 29.8%. These are model-relative priorities, not percentages of explained variance, market impact, or causal contribution. Differences between global priorities and within-component means should not be interpreted without the underlying weighting procedure.

Discussion

The expert-derived framework places customer experience and organizational implementation within the same content marketing agenda. The prominence of satisfaction, motivation, feedback, and innovation suggests that the panel considered useful communication and responsiveness important. This interpretation is compatible with customer-oriented content marketing, but it does not establish that prioritizing these indicators will improve brand equity by any particular amount.

Customer satisfaction ranked highly in both the reported Delphi scores and the separate synthesis output. This convergence is descriptively noteworthy, although the two outputs are not interchangeable. Satisfaction can be an antecedent, correlate, or consequence of customer experience depending on the model being tested. In the present study it is an expert-rated indicator. A later empirical study would need to define its position relative to brand awareness, associations, perceived quality, and loyalty before testing directional relationships. Keller’s (1993) framework helps distinguish brand knowledge from individual marketing activities.

The cultural-readiness and organizational-culture components point to implementation conditions. Willingness to innovate and employee support for change received high scores. These findings are consistent with the emphasis on readiness in Masrianto et al. (2022) and the context-dependent barriers described by Setkute and Dibb (2022). They also suggest that content initiatives may need staff development and management support. Such implications remain propositions drawn from expert opinion, not tested intervention effects.

The content and planning components connect message quality with clear objectives and practical delivery. This relationship is consistent with the customer-journey perspective of Terho et al. (2022) and the strategy-focused analysis of Olson et al. (2021). A leather business could use the framework to distinguish educational content, product information, customer support, and promotional material. The current findings do not justify treating one channel as universally superior across market segments or business models.

The framework includes conceptually heterogeneous indicators. Cultural readiness combines consumer-oriented trust with firm-level capability, while consumer characteristics combine demographic attributes with perceptions and experiences. Resistance to new trends is negatively framed, and no reverse-scoring procedure is documented. Accordingly, the components are thematic categories rather than validated psychometric scales.

The retention of low-rated content-distribution items shows that the retained item set cannot be interpreted as a set of uniformly high-priority factors. Retention reflects the reported expert-selection process, whereas the descriptive means summarize importance ratings. Neither retention nor a high mean establishes consensus in the absence of a defined agreement criterion (Diamond et al., 2014).

Practical implications

The framework can guide an initial review of a firm’s content activities. Managers can map content to customer information needs, use reviews and comments to identify recurring questions, and connect educational material with product use and after-sales support. Brand voice, visual presentation, and claims should be consistent across channels. Any statements about materials, provenance, durability, or environmental performance should be grounded in verifiable product information.

Implementation can be organized around explicit objectives, audience profiles, a content calendar, and assigned responsibilities. Employee training and a team combining marketing, content production, customer relations, and digital communication skills may help coordinate these activities. Customer data should be handled under appropriate confidentiality and information-security arrangements. Social media activity should be selected in relation to the intended audience and communication purpose.

Evaluation should distinguish process measures from outcomes. Views, comments, shares, and conversion activity can describe engagement, while satisfaction and brand-equity measures address different questions. Long-term customer relationships require more than short-term exposure. The reported rankings may help structure discussion, but budget or staffing allocations should not be based mechanically on the unverified weights. Decisions should also consider feasibility, cost, customer evidence, and business objectives.

Limitations and future research

The study relies on a small purposively selected expert group whose characteristics and round-level participation are incompletely reported. The findings cannot be generalized statistically to all leather businesses or consumers. The literature-based item pool lacks a reproducible search trail, and some indicators overlap conceptually. The absence of a prespecified consensus criterion and of respondent-level longitudinal data limits assessment of convergence across rounds.

Recalculation resolved arithmetic differences between recorded frequencies and reported means without modifying the frequencies. It did not resolve the two response totals of 11 or the incomplete SMS-advertising distribution. The hierarchical weights could not be reproduced without the original matrices and calculation settings, and the available sensitivity display did not establish stability under specified perturbations. No consumer survey, validated brand-equity measure, longitudinal outcome, or experimental comparison was reported. Brand-equity improvement therefore remains a proposed application rather than an observed result.

Future research should verify the underlying response records, document panel recruitment and participation, and define retention and consensus criteria prospectively. Independent studies could refine construct definitions, assess content validity, and test the framework with consumer or firm data. Longitudinal or experimental designs would be required to evaluate whether changes in content strategy precede improvements in brand-equity outcomes.

Conclusion

The study organizes 49 retained indicators into nine components within external and organizational domains. Customer satisfaction received the highest final-round importance mean, while willingness to innovate, customer motivation, employee support for change, and low cost also received high ratings. The framework offers an exploratory basis for planning digital content marketing in the leather industry. Its priorities require validation with independently collected data before they can support definitive resource-allocation decisions or claims about improvements in brand equity.

Declarations

Ethical considerations and informed consent

Informed consent was obtained from the expert participants. Participant confidentiality was maintained throughout the study, and responses are reported in aggregate form.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Competing interests

The authors declare no financial or nonfinancial competing interests.

Data availability

The aggregate response frequencies and recalculated descriptive means analyzed in this report are included in Tables 1–9. The reported global priority weights are summarized in the Results section and Figure 1.

Use of artificial intelligence

OpenAI ChatGPT was used to assist with translation, English-language editing, manuscript organization, and identification of numerical inconsistencies. Descriptive means were recalculated from the supplied aggregate response frequencies, and Figure 1 was redrawn from the supplied weights. AI assistance was not used to generate expert responses, impute missing observations, or reconstruct research records.