Web Systems Design and Online Consumer Behavior [Electronic resources] نسخه متنی

Future Trends and Conclusions

In this book chapter, a general framework has been
proposed to address the relationship between objective complexity and perceived complexity and
their impacts on Internet communication effectiveness. Previous literature on objective complexity,
perceived complexity, optimal complexity and cognitive complexity was reviewed. Based on the
literature review, feasible measures of objective complexity, perceived complexity and optimal
complexity are proposed. Finally, seven propositions are generated to chart the relationship within
different types of complexity and between different types of complexity and communication
effectiveness.

Findings from this investigation have significant implications for marketing theory. First,
the investigation suggested feasible instruments that can be tested in future theory-oriented
studies. Second, this investigation challenges the conventional view of complexity. It shows that
complexity is a complicated concept and is necessary to be viewed from the aspects of objective,
perceived and optimal complexity. Third, this research enriches the theory by demonstrating that
even within the aspect of objective complexity, finer dimensions such as component complexity,
coordinative complexity and dynamic complexity needed to be considered. Finally, this research
enriches marketing theory by incorporating insights from information systems research and
psychology research. In this investigation, this cross-disciplinary approach proves to be pivotal
in instrument development and future model testing.

Findings from this investigation have significant implications for marketing practice. First,
this investigation opens a new realm of viewing and managing online communication effectiveness. As
proposed in the study, perceived complexity from consumers are highly but not perfectly correlated
with objective complexity. Hence, e-businesses need to measure both types of complexity
simultaneously in order to identify the optimal complexity properly. Second, the proposed measuring
instrument encourages exploring online communication effectiveness in new depth. For instance, the
specific dimensions of objective complexity convey detailed information that helps explain specific
facets. Third, the present research suggests one important moderating variable, cognitive
complexity. Consumers with higher cognitive complexity are likely to demand a higher objective and
perceived complexity levels to reach the maximum optimal complexity level in comparison to
consumers with lower cognitive complexity. This finding prompts retailers to be sensitive to
differences in consumer characteristics.

This chapter, however, is constrained by the limitation of being highly exploratory in
nature. All the instruments and measures proposed in this study are based strictly on
conceptualization instead of empirical test. The current study, nevertheless, points out the
importance of bridging both user-centered approach and system-centered approach in the
investigation of the new media. Future researchers may want to employ survey or experimental
designs to further explore the complexity issue based on the measures and frameworks proposed in
this study. Furthermore, some of the present conceptual insights may be extended beyond online
communication to all the other complexity areas such as system design, public affairs, social
psychology, behavioral science, information technology, human resources, and advertising where
objective complexity and perceived complexity are likely to be different and hence demand separate
measures.