A DSS framework for maintaining relevant features of the Small Business B2C Websites

Managers are heavily engaged in strategic decision-making for businesses, particularly in a changing environment. One of the most important decisions for online small businesses, as part of their strategic planning, is selecting relevant features on their websites, both to attract and interact with consumers. However, only a few Australian small businesses use strategic tools for selecting their website features. As a result, businesses lose potential domestic sales in the business-to-consumer (B2C) sector. The aim of this study is to determine the relationship between factors that influence consumers’ online purchasing, and owner/manager strategic decisions in selecting relevant features for websites. Results from employing qualitative case studies with small business owner/managers, and a content analysis of website features, inform the design of a Decision Support Systems (DSS) framework. This may assist owner/managers’ strategic decisions to implement competitive features on B2C websites that ultimately attract more consumers.

💡 Research Summary

The paper addresses a critical gap in the strategic management of small‑scale B2C enterprises in Australia: the lack of systematic tools for selecting website features that drive consumer purchases. Recognizing that many owners rely on ad‑hoc judgments focused on cost or technical feasibility, the authors set out to map the relationship between consumer‑centric purchasing drivers and managerial decision‑making, and to translate that insight into a practical Decision Support System (DSS) framework.

A two‑pronged qualitative methodology was employed. First, semi‑structured interviews were conducted with owners or managers of eight small Australian B2C firms to capture their perceptions, priorities, and constraints when choosing website functionalities. Second, a content analysis of each firm’s website catalogued the presence or absence of features commonly linked to online buying behavior—such as real‑time chat, mobile optimization, customer reviews, multiple payment options, and personalized recommendation engines. The analysis revealed a clear divergence: firms that performed well in conversion metrics consistently offered a richer set of consumer‑trust and interaction features, whereas less successful firms limited themselves to basic product information.

Building on these findings, the authors designed a five‑stage DSS framework. Stage 1 involves market and consumer need assessment using surveys, web analytics, and social media listening. Stage 2 generates a comprehensive list of candidate website features, each evaluated for cost, technical difficulty, and expected impact. Stage 3 applies a weighted scoring model that balances these dimensions, producing a cost‑effectiveness index for every feature. Stage 4 uses the index to prioritize features within a constrained resource environment, effectively creating a decision matrix that highlights the optimal feature mix. Stage 5 delivers an implementation roadmap and establishes a feedback loop for post‑deployment monitoring and continuous improvement. The framework is deliberately flexible: weightings can be adjusted through expert panels or stakeholder surveys, allowing firms of varying size and industry to tailor the tool to their specific context.

The study contributes both theoretical and practical insights. Theoretically, it extends the e‑commerce literature by empirically linking specific website functionalities to consumer purchase intent within the small‑business segment. Practically, it offers a low‑cost, replicable DSS that empowers owners to make evidence‑based, strategic choices about website design, thereby enhancing competitiveness and domestic sales potential. Limitations include the geographic concentration of the sample and reliance on self‑reported data; future research should test the framework across broader markets and quantify performance gains through longitudinal studies. In sum, the paper delivers a rigorously grounded, actionable decision‑support model that fills a notable void in small‑business digital strategy.