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While prior studies have explored the relationship between visual appeal and purchasing decisions, the role of bookmarking has largely been underemphasized. This research aims to address this gap by focusing on the impact of bookmarking on consumer behavior, guided by the cognitive load theory and dual-system theory.

The authors executed a controlled experiment and analyzed the results using a two-stage regression method that linked visual appeal, bookmarking and purchase intent. Further empirical analysis was conducted to authenticate the authors' proposed model, utilizing real-world mobile commerce data from a clothing company.

This study's findings suggest that visual appeal influences purchase intent primarily through the full mediation of bookmarking, rather than exerting a direct influence. Furthermore, an increase in colorfulness corresponds positively with visual appeal, while visual complexity exhibits an inverted U-shaped relationship with it.

This study provides novel insights into the choice-set formation process through the theoretical lens of dual-system theory. Additionally, the authors employed an image processing technique to quantify a product's visual appeal as depicted in a photograph. This study also incorporates a comprehensive econometric analysis to connect the objective aspects of visual appeal with subjective responses.

Consumer expectation not only influences purchase decision but also post-purchase satisfaction and word-of-mouth (WOM). This study aims to develop theories of initial expectation management by suggesting when it is desirable for new products to raise or lower consumer expectations. It systematically examines the interplay of product value and consumer heterogeneity in the dynamic process of new product diffusion under competition.

Drawing on traditional diffusion and choice models, this study develops an agent-based model to formalize and analyze how consumers’ initial expectations of a new product influence the interdependent processes of product sales, consumer satisfaction and WOM. The simulation analyses in controlled settings help understand the underlying mechanisms in a stepwise manner.

The results show that, although the optimal strategy for low-value products is to induce consumer expectations higher than product value, high-value products are better introduced with expectations formed close to it. The results also highlight an important drawback of “under-promising” strategies in reducing the base and volume of WOM. Further, the analysis illustrates how consumer heterogeneities in product valuation and initial expectation affect the effectiveness of expectation management. For high-value products, both heterogeneities reduce the effectiveness of the optimal strategy. For low-value products, however, value heterogeneity enhances the effectiveness, whereas expectation heterogeneity reduces it.

Firms introducing new products should be sensitive to how consumers value the product and form expectations about it. Different from firms that must rely on aggressive advertising to sell inferior products by building up high expectations, those with superior products can rely more on the power of consumer WOM, which is much less costly and thus gives them a competitive advantage. Firms should also pay attention to how diversified the consumers are in product valuation and expectation. The expectation management strategy is more effective when consumers form more similar expectations. Inferior firms may leverage this mechanism to neutralize their disadvantages.

The articulated mechanisms help push forward the research on new product diffusion and consumer expectation management. To the best of the authors’ knowledge, this is one of the first studies to systematically analyze the impact of consumer heterogeneity on the effectiveness of expectation management.

The paper proposes first, to understand how and how much knowledge contributes toward explicit business performance improvement and, second, through the understanding of knowledge contribution, to provide a guiding principle for the effective knowledge management activities.

The authors use a Cobb‐Douglas type production function to model the relationship between knowledge and performance. Then, regression analysis is used to estimate the knowledge elasticity of performance. Finally, a laboratory experiment is used to demonstrate the whole process.

A performance‐oriented knowledge management approach was developed. Through the analysis of knowledge‐intensive production function, it is shown that the knowledge elasticity of performance for each knowledge entity (product knowledge and process knowledge) can be estimated and can be used with great managerial implications.

Extensive empirical analyses in the real world business environment would be helpful to verify and generalize this approach.

The paper demonstrates the specific process of how to measure the contribution of knowledge to performance, and provide a guiding principle for the effective knowledge management activities.

As far as the authors understand, this is the first systematic and complete approach to analyze and estimate the contribution of knowledge to performance. Using the production function approach, it was possible to estimate the knowledge elasticity of performance, which provides valuable insight on the resource allocation for knowledge management activities.