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Recent research into effectuation and effectual logic show that entrepreneurs think and make decisions dramatically different than typical enterprise managers. One of the major issues in applying entrepreneurship and innovation to corporations is likely the misunderstanding and failed application of these core concepts to management practices. Thus, those studying intrapreneurship and innovation would find great value from this paper's discussion of effectuation and effectual logic as it explains its major differences between entrepreneurs and typical enterprise manager views as they pertain to: Goal setting, risk taking, resource selection and gathering, dealing with setbacks, building networks, and management control. To dive deeper into these valuable concepts, effectuation and its core principles were applied to the product development process and systems development process. This includes applying them to: Screening of ideas, business analysis, development, product validation, and the market launches phase of product development. This article should help those applying entrepreneurship practices to their organizations and result in more innovative managers and employees, or as the authors term it "enterprise entrepreneurs."

Organizational learning is influenced by many traits. Among these, certain environmental and managerial traits within the organization have been shown to support a learn- ing organization. This organizational learning is crucial in infor- mation system development so that future projects may gain from the knowledge generated from past projects and during current projects. Organizational technology learning is the term we give to the organizational learning that occurs in information system development. A survey of project managers and participants in sys- tem projects tested whether certain common traits tend to be active promoters of learning in the project team setting as well as at the organizational level. Of those traits tested, clear mission and objec- tives, knowledge transfer mechanisms, and experimentation all proved to be significant environmental factors for learning in proj- ect teams.

The research results on group consensus have been ambiguous within the field of Group Support Systems (GSSs). Some research studies have shown that groups using multiple communication channels produce more ideas, and more unique ideas than groups using a single communication channel. In addition, a second set of research findings have shown that groups using GSSs report group members hold “less consensus” with the group's decision. This research studies more closely how these two characteristics; communication channel and consensus, interact. Specifically, can “less consensus” be a function of the choice of channels used in problem solving. The results show that groups using a single communication channel generate more actual consensus than groups using multiple communication channels. Furthermore, the single channel discussions provide more integrative comments and these integrative comments may help explain the difference in consensus. These results suggest that those striving for consensus from group members should consider production methods used to create the information that is to be used in the decision.

Companies are beginning to realize that simply storing data in warehouses and databases is not sufficient to ensure the usefulness of that data or information. As information is processed with a purpose (Ackoff & Emery, 1972), it becomes knowledge. Knowledge exists on many planes; one is the tacit-explicit plane described by Polanyi (1966). Knowledge-based applications are becoming a key factor in determining organizational value. For example, Activity-Based Costing (ABC) and Knowledge Value Added (KVA) are two methodologies that organizations use to measure explicit knowledge. However, this interest in measuring and managing knowledge creates two important questions. First, do companies include tacit knowledge in their measurements of knowledge? Second, can educators demonstrate that they are helping increase tacit knowledge? This paper posits that companies may not be including the full range of knowledge in their endeavor to measure knowledge in their organizations. To help make a case for including tacit knowledge and implicit learning, an active learning exercise is created to demonstrate a method to quantify and test for changes in both types of knowledge. The results show that the activity created observable changes in explicit and tacit knowledge. Future research will need to concentrate further on understanding how the two types of knowledge interact. For now though, these results demonstrate one way to observe the two types of knowledge and more importantly, point out the need for organizations to find ways to value both implicit and explicit knowledge.

This study compared two tabular presentations and one graphical presentation of the same rank order data to study differences in participant comprehension. Hypothetical raw voting data by ten different judges ranking ten states on the desirability of each state as a preferred place for retirement was used. Three sets of the data were constructed for the voting scenario with low, high and moderate levels of consensus. The summary data was a table (TABULAR) representing the total count of votes for each rank (first place through tenth place) for all ten states. It was also presented as a 3D cone infographic (GRAPHICAL) and raw voting data (RAW). Participants were asked to estimate the level of agreement (0.00 to 1.00) indicated by each presentation. The results showed that the graphical and tabular presentations differed in participant comprehension for low and moderate levels of agreement but not for high levels of agreement.

Much of the Group Support System (GSS) literature comparing GSS groups with manual groups has produced contrarian results. One of the more confusing is that GSS groups have a higher level of non-consensus than manual groups. Lack of consensus in GSS groups is considered to be a negative aspect of GSS. This paper argues that low levels of consensus are not necessarily harmful and should be expected given the assumptions about GSSs. This study uses an alternate measure to compare face-to-face groups with GSS groups; an influence level of information. Experimental results using an influence level of information show no difference between face-to-face group members and GSS group members. We discuss these implications, as well as additional directions for further consensus research.

Over the past several years, a number of research studies have investigated the application of group support system (GSS) technology to the classroom. The purpose of this paper is twofold: (1) to summarize and assess what has been learned from this emerging body of research, and (2) to draw on the GSS and cooperative learning literatures to construct a framework to guide future studies. The review of research studies reported in this paper focuses on comparative studies investigating the synchronous GSS classroom environment. While research findings regarding the impacts of GSS on learning performance are somewhat mixed, early indications are encouraging and suggest that GSS may have a positive impact under certain situations. A framework based on the GSS and cooperative learning research is introduced that identifies the contextual, group process, and outcome factors that researchers may wish to consider when designing and interpreting future research studies in this area.

This is the second part of a longitudinal study that examines how employee Internet abuse may be reduced by non-technical deterrence methods, specifically via IT acceptable use policies (AUP). Both studies used actual usage and audit logs (not self-reporting measures) to monitor the web activity of employees. In the earlier study, a mild AUP reminder to company employees resulted in a 12 percent decrease in non-work Internet usage. The current study utilized a more severe AUP communication and resulted in a 33 percent decrease in non-work Internet usage. For both studies, the AUP reminder resulted in an immediate decrease in non-work Internet usage. Results indicate that while non-work traffic under both treatments returned over time, the longevity effect of the severe AUP message was greater than the mild AUP message and non-work traffic did not return to its previous pre-treatment level by the end of the study.

This chapter explores the concern for social needs in distance education. As a foundation, the chapter discusses the evolving learning environment and stakeholder expectations that distance education must address as it grows in prevalence. In addition, technology is a key enabler of any distance learning program. This chapter analyzes several theories that integrate learning and technology for potential insights applicable to distance learning. These insights lead to an exploratory study to compare the “need for affiliation” between students working in groups for distance classes (virtual groups) and students working in groups for on-campus classes (actual groups). The preliminary results of the study indicate that some issues concerning socialization do exist between the two groups. Ultimately, the study points to the need for more formal and definitive measures of the social aspects of group work in distance education.

Almost 3.5 million students were taking at least 1 online course during the fall 2006 term. The 9.7 % growth rate for online enrollments far exceeds the 1.5 % growth of the overall higher education student population. (Allen and Seaman, 2007) By 2006, the distance education industry was well beyond $33.6 billion (Merit Education, 2003). As with most markets, 1 of the keys to taking advantage of this growing market is customer satisfaction. Therefore the greater the student satisfaction in a distance program, the more likely that program will be successful. This paper identifies 5 key components of satisfaction for distance education programs through a student satisfaction questionnaire and factor analysis. A questionnaire was developed using these variables and administered to 341 distance students. The results revealed 5 constructs for student satisfaction in a distance education program (Martz and Reddy, 2005; Martz and Shepherd, 2007). Using these factors as guidance, this paper extends those findings to provide some operational and administrative implications.

Most researchers, practitioners and theorists describe group problem solving as having a divergent phase (production) and a convergent phase (selection). Research has shown that groups using multiple communication channels produce more ideas, and more unique ideas than groups using a single communication channel. The research study described here attempts to extend this theme and more closely examine the impact of the production phase on the selection phase of group problem solving. The results show that groups using a single communication channel generate more actual consensus than groups using multiple communication channels. In addition, the single channel discussions provide more integrative comments and these integrative comments may help explain the difference in consensus.

Much of the GSS literature comparing GSS groups with manual groups has produced contrary results. One of the more confusing is that GSS groups have a higher level of non-consensus than manual groups. Lack of consensus in GSS groups is considered to be a negative aspect of GSS. This paper argues that non-consensus is not necessarily harmful, and proposes an alternative interpretation of non-consensus using a measure other than actual consensus: an influence level of information. Experimental results show GDSS provide more influential information than manual groups and that measuring influential information can be a better predictor of satisfaction than actual consensus. We discuss these implications, as well as additional directions for further consensus research.

According to Martin and Samels (Martin & Samels, 1995), distance education is one of the least understood areas of technological change in higher education today. The constant improvement in technology, the rapidly changing student demographics, the constant demand for better educated students, the need for more Information Technology (IT) professionals, and the lack of more universally accepted definitions and learning models have all created a wealth of opportunities in this field. One of the challenges for researchers in this field is finding an accepted definition for “distance education.” For some (Keegan, 1986; Verduin & Clark, 1991), distance education takes place whenever the instructor and student are physically distant from each other. Others (Baird & Monson, 1992) require

This paper attempts to create a research cycle for Problem Based Learning and in so doing help substantiate the use of PBL in business school environments. The paper starts with a review of learning and memory in order to extract key concepts needed for the analysis. Memory concepts extracted centered on the concept of analogy or metaphor and of memory optimizing constructs such as scripts and templates. The processes of assimilation and appropriation were learning concepts retrieved. Second, the overlap between action research and PBL was used to derive an action research cycle for PBL. Next, the cycle's viability was demonstrated by mapping an actual PBL activity to the new cycle. Finally, the paper positions PBL and more specifically, the research cycle for PBL, in light of the ongoing changes in the general business school environment. In total, the paper presents a model for using and justifying PBL in business curricula.

Distance education is big business; more than 1.6 million students enroll in distance education classes annually. The Institute of Higher Education (2000) estimated that by 2004 more than 90% of all two- and four-year colleges will offer some sort of online courses. By the end of 2003, the virtual education market will grow in excess of $21 billion (Svetcov, 2000). New technologies and improvements in networking capabilities are enabling distance education instructors to come closer to providing the traditional learning environment for their students. However, many issues still need to be resolved and, according to recent research studies, it does not appear that these will be resolved any time soon.

The issue of consensus is unclear in technology-supported meetings. Research indicates that technology supported groups have less consensus than face-to-face groups, and that it is the technology that is at least partly to blame for the non-consensus process loss. This paper reports on an experiment that addresses the issue of how well individuals understand consensus. The results show overwhelmingly that individuals neither agree on what consensus looks like nor do they apply a consistent interpretation of consensus. In addition, the lack of consensus in technology-supported groups may not be due to the technology at all, but rather to the incorrect assumption that individuals know consensus when they see it.

A combination of stakeholder expectations and an evolving learning environment drive the interest in distance education. Technology is the key enabler of distance education by removing the geographical and temporal constraints imposed by traditional classroom environments. Today, distance education is struggling to manage and to meet the expectations of all of its stakeholders. For example, state organizations want to use taxpayer dollars efficiently; instructors want to present course topics for effective learning; and students want to use their learning to enhance their careers. It is safe to say that ultimately all parties want the best outcome. The biggest problem is that one is sure of all the parts or how those parts fit together.