A comprehensive empirical evaluation of automotive enterprises demonstrates that corporate digital maturity significantly influences customer relationship management outcomes, yet higher technological sophistication does not automatically generate massive divergence in overall performance grades. In a study published in Discover Analytics, researcher Bo Tang investigated how automotive manufacturers and supply chain stakeholders translate digital transformation initiatives into measurable customer relationship management performance. The automotive sector represents a critical pillar of modern global manufacturing, characterized by multi-tiered value networks that encompass raw material vendors, component manufacturers, vehicle assembly plants, dealership networks, and post-sale service operations. As consumer preferences diversify and demand greater personalization, automotive organizations increasingly depend on digital architectures to acquire, retain, and serve clientele. Tang established that although companies with advanced digital integration achieve superior ratings in specific client-facing dimensions, firms across distinct maturity tiers often register remarkably similar overall performance scores, highlighting nuanced operational realities governing modern industrial digitalization.
Managing customer interactions across modern industrial supply chains involves persistent strategic challenges, particularly because customer relationship management implementation initiatives historically suffer from failure rates exceeding sixty-five percent. Customer relationship management initially emerged as a structured marketing concept during the nineteen-seventies, focusing on strategic policies, analytical modules, and operational workflows designed to maximize lifetime customer value, secure long-term buyer retention, and discover profitable distribution channels. In early corporate development, small customer bases could be maintained through direct manual tracking, but modern enterprises must deploy sophisticated digital platforms to capture initial orders and coordinate complex commercial transactions. Despite massive capital outlays directed toward software infrastructure, corporate decision-makers frequently struggle to determine whether continuous technology investments yield proportional organizational advantages. Tang formulated four core research questions to clarify these dynamics: whether automotive firms possessing advanced digital maturity attain demonstrably superior customer relationship outcomes, whether higher maturity tiers fail to show significant performance divergence, and whether digital tools implemented within less mature operating environments still provide meaningful organizational assistance.
To establish rigorous analytical boundaries, the investigation evaluated digital maturity through the formal Integration of Informatization and Industrialization certification framework propagated by China’s Ministry of Industry and Information Technology. Under this standardized industrial benchmark, an enterprise achieving an AAA-level certification has demonstrated complete, end-to-end value chain connectivity powered by synchronized digital platforms spanning every major operational division. In contrast, organizations holding AA-level or lower certifications maintain localized digitalization restricted to isolated business departments without seamless enterprise-wide data interoperability. In automotive value chains, full digital connectivity encompasses an extensive ecosystem of specialized software suites across four essential domains. In research and development, firms utilize product life-cycle management, product data management, bill of material systems, and internet of vehicles connectivity. Manufacturing operations rely on enterprise resource planning, enterprise asset management, manufacturing execution systems, and supervisory control and data acquisition architectures. Supply logistics incorporate supplier relationship management, warehouse management, logistics execution, and transportation management systems, while customer service centers, after-sales service platforms, and spare-parts sales mechanisms anchor downstream marketing activities.
Evaluating organizational performance across qualitative and subjective dimensions necessitated a mathematical modeling technique capable of handling descriptive ambiguity rather than relying strictly on continuous financial time-series forecasting. While algorithmic approaches such as Gaussian process regressions effectively evaluate continuous price fluctuations, expert human assessments regarding organizational efficacy contain qualitative, non-quantified nuances that standard numerical models obscure. Tang employed a multi-grade Fuzzy Comprehensive Evaluation model integrated with the Analytic Hierarchy Process and triangular fuzzy numbers to transform subjective professional judgments into systematic mathematical matrices. The assessment architecture featured a two-layer structural framework organized around four primary first-class indexes: increasing customer satisfaction, reducing operating costs, expanding market share, and improving digital maturity. These four overarching categories were further subdivided into thirteen secondary sub-indexes capturing specific operational parameters across value chain activities. Standardized pairwise comparison matrices determined relative factor weights by calculating maximum eigenvalues and corresponding eigenvectors, while a four-tier semantic scale graded corporate performance across designated thresholds labeled Excellent, Good, Fair, and Poor.
The empirical dataset comprised structured feedback collected from two hundred automotive industry professionals representing diverse corporate tiers, ensuring balanced cross-sectional representation across operational roles. Because relatively few industrial manufacturing entities successfully attain elite AAA-level certification while the broad majority of market participants hold AA-level or lower credentials, respondents from AAA-certified firms and lower-certified firms were deliberately sampled at an approximate ratio of one to three. The primary survey cohort consisted of industry practitioners possessing three to five years of specialized operational experience, representing front-line professionals who directly engage with digital platforms and intimately comprehend technological workflows. Top-level executive leaders and senior departmental managers accounted for smaller, highly concentrated proportions of the sample, providing strategic executive validation regarding overarching digital performance outcomes that mirrored analytical perspectives commonly reported by prominent global management consultancies.
Quantitative computational results for the cohort of AAA-level certified enterprises demonstrated an aggregate fuzzy comprehensive evaluation score of zero point seventy-eight, situating the high-maturity group firmly within the second-tier semantic rank designated as Good. When evaluating individual first-class performance vectors, scores across the four core dimensions ranged between zero point seventy-five and zero point eighty-one. The highest individual performance appeared in the first-class indicator evaluating customer satisfaction enhancement, which attained an evaluation value of zero point eighty-one and earned the highest qualitative rating of Excellent. This elevated score demonstrated that comprehensive digital integration throughout the value chain delivers tangible external benefits by providing consumers with responsive, intelligent, and user-friendly digital interfaces that modern clients consistently prefer over antiquated telephone or email communications. However, the remaining three primary indicators—operating cost reduction, market share expansion, and digital maturity improvement—all registered scores within the Good threshold, revealing that while customer-facing digital touchpoints achieve immediate excellence, internal operational adjustments remain comparatively demanding for front-line industrial staff members.
For the enterprise cohort holding AA-level and lower certifications, the mathematical evaluation produced an overall composite fuzzy comprehensive evaluation score of zero point seventy-three, similarly categorizing these organizations within the Good semantic bracket. The four primary first-class indexes exhibited closely clustered scores ranging from zero point seventy-two to zero point seventy-six, indicating that localized digital applications still generate positive operational contributions across less digitally integrated automotive firms. Survey distributions highlighted that respondents from less digitally mature enterprises submitted noticeably higher frequencies of Poor evaluations and substantially fewer Excellent ratings regarding operational software integration. Although digital tools successfully assisted lower-maturity companies in coordinating fundamental supply chain interactions and managing client touchpoints, their inability to achieve top-tier satisfaction metrics revealed distinct operational bottlenecks inherent in fragmented corporate digitalization.
Tang analyzed the underlying structural drivers responsible for this customer satisfaction gap, identifying an acute misalignment between sophisticated software tools and immature organizational business processes within lower-tier enterprises. When automotive companies possessing rudimentary operational processes introduce standardized, complex digital architectures, front-line employees and shop-floor supervisors frequently resist abandoning familiar manual documentation routines, resulting in awkward workflow handoffs and fragmented data flows. Consequently, immature internal operational workflows inadvertently force external clients, suppliers, and distribution partners to undertake redundant administrative burdens during initial supply chain exchanges. While enterprises characterized by advanced digital maturity exhibit harmonious co-existence between technological platforms and mature operational processes, lower-maturity organizations frequently impose cumbersome digital systems upon unrefined operational frameworks. Over extended deployment horizons, continuous refinement of internal processes can gradually align corporate workflows with digital interfaces, but uncoordinated initial adoptions persistently depress client satisfaction, generating user frustration toward rigid software environments that fail to accommodate real-world shop-floor practices.
The empirical patterns documented within the automotive analysis closely align with broader historical case studies illustrating digital transformation difficulties across global heavy industry. Sizable capital outlays in information technology frequently generate unpredictable productivity returns when corporate architectures fail to integrate technological capabilities with human operational habits. Prominent historical examples referenced in transformation literature underscore the severe strategic risks associated with premature technological adoption, including General Electric’s multibillion-dollar restructuring and downsizing of its Predix industrial software platform following missed operational targets, as well as Ford’s internal alignment challenges throughout its ambitious Smart Mobility enterprise program. Industry experts participating in the survey emphasized that technological investments executed without preliminary workflow re-engineering, robust corporate governance, and precise operational role delineation routinely fail to achieve projected efficiency gains. In contrast, global benchmarks such as automotive supplier Bosch exemplify successful digital progression by systematically harmonizing organizational processes with technological capabilities, thereby sustaining meaningful digital innovation without creating disruptive operational dissonance.
In synthesizing the empirical findings, the study outlines essential practical considerations for governmental policymakers and manufacturing executives navigating modern digitalization initiatives. Policy authorities must champion standardized business process maturity evaluations before mandating or promoting universal industry-wide digital platform adoptions across small and medium-sized industrial suppliers. For enterprise leaders, conducting comprehensive internal workflow maturity audits prior to executing major software capital investments represents an indispensable requirement to prevent costly project failures and organizational friction. Tang concluded that digital transformation must be treated as a holistic managerial evolution rather than an isolated acquisition of software licenses, emphasizing that technological benefits fully materialize only when internal operating procedures mature alongside technical infrastructure. Future research trajectories proposed in the study include longitudinal examinations of customer relationship performance utilizing machine learning and deep learning methodologies, comprehensive investigations into the precise structural linkages connecting process maturity to commercial success, and expanding multi-grade fuzzy evaluation frameworks to assess customer relationship dynamics across manufacturing sectors beyond automotive production.
Subject of Research: Evaluation of customer relationship management performance across automotive firms with differing digital maturities
Article Title: Customer relationship management (CRM) performance evaluation in automotive industries with different level of digital maturities
Article References: Tang, B. (2026). Customer relationship management (CRM) performance evaluation in automotive industries with different level of digital maturities. Discover Analytics, 4(1), Article 15. https://doi.org/10.1007/s44257-026-00063-8
Image Credits: AI Generated
DOI: 10.1007/s44257-026-00063-8
Keywords: automotive industry, customer relationship management, digital maturity, fuzzy comprehensive evaluation, analytic hierarchy process, supply chain connectivity, digital transformation, business process maturity, Customer, relationship, management, performance
Source: bioengineer.org
