Raising the web conversion rates for online ventures with no name recognition: analytical and empirical studies

This research aims to build a theoretical model of the salient factors affecting the purchase behaviour of visitors to online retailers without an existing reputation. The proposed model, referred to as the conversion behaviour model (CBM), has been developed with two major objectives. The first objective is to provide insight into consumer conversion behaviour within an ecommerce context. The second objective is to provide a theoretical foundation upon which ecommerce managers and web developers can build practical procedures to improve web conversion rates. Three major questions, based on these objectives, guide the current research. The first question is “What are the salient factors involved in the purchase decisions for first-time visitors to an online vendor without reputation?” The second follows from this; “What are the inter-relationships among these determinant factors and their association with consumer purchase intention?” The final question covers the application aspect; “How can ecommerce managers control these factors to improve the website conversion rates?” The research first reviews the literature related to online shopping adoption from the perspective of several “parent” disciplines. A classification model based on this review reveals three streams in online consumer behaviour research; human-computer interactions (HCI), online purchase decision process (OPDP), and web marketing-mix strategies. The review also suggests that the Conversion Behaviour Model should be developed mainly based on the second stream (OPDP). Research within the OPDP stream is consequently further considered. Most leading models of consumer behaviour online are adapted from Theory of Reasoned Actions (TRA), the Theory of Planned Behaviour (TPB), Technology Acceptance Model (TAM), and theories of trust in the Internet (Javenpaa et al., 2000) A conceptual model of CBM is proposed, based on this literature, where Trust and Perceived risk enhance online Consumer Purchase Intention. Trust and Perceived risk, in turn, are dependent on Perceived web interface quality and Perceived social acceptance. Published literature in the information systems and marketing fields are reviewed to demonstrate empirical support for the proposed model’s theoretical constructs, while at the same time showing that the proposed model goes beyond the existing theoretical specifications by explaining and integrating previous research issues in a cumulative manner. A C-OAR-SE-based scale development procedure is then established to validate the causal relationships within the proposed model. The C-OAR-SE procedure prompts the researcher to pay more attention to scale validity and attribute nature than to internal consistency; this is contrary to most prior research on online consumer behaviour which conventionally focuses on high coefficient alphas (internal reliability). Data collection is conducted in Viet Nam where the ecommerce market has blossomed but financial constitutions for e-transactions are not yet in place. A dummy Internet site is constructed and housed on a real trading host server, and “real” online customers are driven to the research site. The collected data is analysed by two different meta-analysis methods, the mediation test (Baron & Kenny, 1986) based on the scale enumeration rules of Rossiter (2002) and a structural equation model, to examine the causal linkages of the theoretical model. The results show trust partially mediates perceived web interface quality and perfectly mediates perceived social acceptance on purchase intention. In contrast to prior research concerning trust and perceived risk in an Internet setting, the results show the perfect mediation of trust on perceived risk to purchase intention. Comparing the two data analysis methods in the current study, the results show the mediation test produces more significant causal relationships than the SEM model; the SEM model on C-OAR-SE-based scales should be subject to further conceptual and empirical studies. Results of the current study also suggest a procedure that ecommerce managers and website developers could follow to improve the conversion rate of their websites