Adaptive learning algorithm of self-organizing teams

In order to improve the ability of achieving good performance in self-organizing teams, this paper presents a self-adaptive learning algorithm for team members. Members of the self-organizing teams are simulated by agents. In the virtual self-organizing team, agents adapt their knowledge according to cooperative principles. The self-adaptive learning algorithm is approached to learn from other agents with minimal costs and improve the performance of the self-organizing team. In the algorithm, agents learn how to behave (choose different game strategies) and how much to think about how to behave (choose the learning radius). The virtual team is self-adaptively improved according to the strategies’ ability of generating better quality solutions in the past generations. Six basic experiments are manipulated to prove the validity of the adaptive learning algorithm. It is found that the adaptive learning algorithm often causes agents to converge to optimal actions, based on agents’ continually updated cognitive maps of how actions influence the performance of the virtual self-organizing team. This paper considered the influence of relationships in self-organizing teams over existing works. It is illustrated that the adaptive learning algorithm is beneficial to both the development of self-organizing teams and the performance of the individual agent.     

Genetic learning of virtual team member preferences

Virtual team members do not have complete understanding of other team members’ preferences, which makes team coordination somewhat difficult and time consuming. Traditional approaches for team coordination require a lot of inter-agent electronic communication and often result in wasted effort. Methods that reduce inter-agent communication and conflicts are likely to increase productivity of virtual teams. In this research, we propose an evolutionary genetic algorithm (GA) based intelligent agent that learns a team member preferences from past actions, and develops a team-coordination schedule by minimizing schedule conflicts between different members serving on a virtual team. Using a discrete event simulation methodology, we test the proposed intelligent agent on different virtual teams of sizes two, four, six and eight members. The results of our experiments indicate that the GA-based intelligent agent learns individual team member preferences and generates a team-coordination schedule at a lower inter-agent communication cost.     

The process of team communication in multi-cultural contexts: An empirical study using Bales’ interaction process analysis (IPA)

The main goal of the present study was to empirically investigate the manner in which cultural diversity and communication mode affect team communication process. Two independent variables were manipulated in the study: (1) cultural diversity (homogeneous versus heterogeneous); and (2) communication mode (face-to-face versus computer-mediated communication). Team members’ communication exchanges were measured by using Bales’ interaction process analysis (IPA) that divides the function (as opposed to the topical content) of communication during group discussion into either task-related or socio-emotional contributions at two-levels: communication profile and functional area. Results of this study indicated homogeneous teams used more showing agreement, giving opinions, and showing tension communication patterns compared to heterogeneous teams. Additionally, when considering the higher-level functional areas, teams using face-to-face (FTF) communication strategies were characterized by greater positive socio-emotional communication, more attempted answers, more questions, and more frequent negative socio-emotional communications. The results of the present study also suggested that the impact of communication mode on team communication varies by the level of diversity within the team. The outcomes of this research should have a broad impact on the management of a multi-cultural team, a task-oriented team consisting of people with different national cultures. Relevance to industry: Results of the present study should provide valuable insights into the ways in which cultural diversity and communication mode influence the communication mechanisms which take place among members during team interaction.     

Conceptualizing the Awareness of Collaboration: A Qualitative Study of a Global Virtual Team

Innovative organizations are increasing their use of distributed teamwork, but there are several difficulties in reaching shared understanding between the team members in these settings. A lack of awareness of other team members’ working processes is one of the drawbacks that a virtual team may face while attempting to collaborate on a shared task. In this study virtual teamwork was supported with a specific working model. The aim was to investigate virtual team members’ awareness of collaboration. One global team (N=19) within a single organization worked as a distributed team in a shared web-based workspace for three months. The data were gathered by means of questionnaires, log-files of the shared virtual workspace and collected company documents in order to find out how team members perceive their collaboration. Based on qualitative data analysis, three different aspects of collaboration awareness were identified: an awareness of the possibility for collaboration, an awareness of the aims of collaboration, and an awareness of the process of collaboration. The results presented in this paper give guidelines for discussing what the awareness of collaboration means in the context of distributed collaboration.     

Extraction of user profile based on workflow and information flow

A collaborative team usually consists of team members with various domains. These members’ demands for knowledge are also different from each other. For recommending potentially useful knowledge to suitable members, their user profiles should be well managed and maintained. User profile can be input by the members, but a more intelligent way should be the automatic extraction of the user profiles. Workflow and information flow are two types of collaborative processes, which exist behind every collaborative team. This paper is mainly concerned with how to extract these team members’ user profile from the two types of contexts: workflow and information flow. This paper defines a model for the user profile. Then some methods are proposed for extracting the profile information on the basis of workflow and information flow. This study on the user profile extraction can pave the way for developing knowledge recommender systems, which can recommend proper knowledge to proper team members with a collaborative team.     

The yellow line: a critical study of the symbolic value of artefacts in health care teams

Artefacts reveal an organisation’s “inner life” and they contribute to its image and reputation. They also play a decisive role for an organisation’s development. In this article, similar artefacts from two different health care teams—a trauma team and a psychiatric team—in the same hospital, are compared. The team members were interviewed and their work observed over the course of several years. It was demonstrated that identical pieces of equipment in a trauma team and a psychiatric team signalled opposite values. The psychiatric team was backward-looking, conservative and contradictory. Modern technology and pieces of equipment were associated with an abandoned and previously criticised activity. The corresponding equipment in the trauma team, on the other hand, signalled a forward looking, developing and unified culture. The trauma team was a relatively new and powerfully idealised phenomenon, which attracted attention. The analysis points out how the symbolic values signal that one activity is attractive and pleasing while another has a low external legitimacy.     

Mining knowledge demands from information flow

Among a collaborative team, members usually come from diverse disciplines, and their demands for knowledge are also different from each other. Information flow is a type of collaborative process, which exists behind every collaborative team. This paper is concerned with how to obtain team members’ knowledge demands from the information flow. Firstly, the knowledge demands model is defined. Based on the model of knowledge demands and information filtering technologies, some approaches for mining demands from information flow are proposed. This study on the knowledge demand mining can pave the way for developing knowledge recommender systems, which can recommend proper knowledge to proper team members with a collaborative team.     

Determinants of successful virtual communities: Contributions from system characteristics and social factors

Although there is growing interest in virtual communities, few studies have examined them from an integrated viewpoint including technical and social perspectives. By expanding on DeLone and McLean’s IS success model, the author constructed a model of the impact of system characteristics (e.g., information and system quality) and social factors (e.g., trust and social usefulness) in implementing successful virtual communities. Data collected from 198 community members provided support for the model. Results showed that both member satisfaction and a sense of belonging were determinants of member loyalty in the community. Additionally, information and system quality were found to affect member satisfaction, while trust influenced the members’ sense of belonging to the community. Finally, the findings provided understanding of the factors that measured virtual community success. Implications of my study are discussed.     

Exploring the antecedents of team performance in collaborative learning of computer software

As teamwork becomes common in computer software learning, there is an increasing need to study factors contributing to team performance. To this pursuit, the concept of collective efficacy offers great potential to researchers of information systems (IS). Drawing upon social cognitive theory (SCT), this study examines the relationships among computer collective efficacy (CCE), outcome expectations and team performance in the context of collaborative learning. Computer collective efficacy is further divided into two constructs, general CCE and specific CCE. Their causal relationships with two other constructs, performance and outcome expectations, are then investigated using longitudinal data collected from 188 groups of students of an 18-week computer software course. The results indicate that a team’s software learning performance is strongly influenced by specific CCE and outcome expectations, which in turn are influenced by prior performance and general CCE. Finally, prior performance of a group has no direct impact on its subsequent performance.     

A System for Building Intelligent Agents that Learn to Retrieve and Extract Information

We present a system for rapidly and easily building instructable and self-adaptive software agents that retrieve and extract information. Our Wisconsin Adaptive Web Assistant (WAWA) constructs intelligent agents by accepting user preferences in the form of instructions. These user-provided instructions are compiled into neural networks that are responsible for the adaptive capabilities of an intelligent agent. The agent’s neural networks are modified via user-provided and system-constructed training examples. Users can create training examples by rating Web pages (or documents), but more importantly WAWA’s agents uses techniques from reinforcement learning to internally create their own examples. Users can also provide additional instruction throughout the life of an agent. Our experimental evaluations on a ‘home-page finder’ agent and a ‘seminar-announcement extractor’ agent illustrate the value of using instructable and adaptive agents for retrieving and extracting information.     

A model to support the search for consensus with conflicting rankings: Multitrident

A general problem of collective decision‐making can be formulated in terms of the N rankings of a discrete set of alternatives expressing the preferences of the N members of the group. Helping to reach a consensus is quite important for team building and organizational development and several contributions were developed by different disciplines like organizational sciences, human resources management and operational research. However, very few models were proposed to support the process of reaching consensus and such models assume a concept of distance between rankings implying an assumption of equal distance between adjacent alternatives. Unfortunately, this hypothesis is hardly acceptable in most cases. In this paper, a model is proposed to search for a ranking in terms of the weights space avoiding the previous assumption and providing useful insights to help decision‐makers adopting such ranking as a consensus decision.     

Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography

Ensemble methodology, which builds a classification model by integrating multiple classifiers, can be used for improving prediction performance. Researchers from various disciplines such as statistics, pattern recognition, and machine learning have seriously explored the use of ensemble methodology. This paper presents an updated survey of ensemble methods in classification tasks, while introducing a new taxonomy for characterizing them. The new taxonomy, presented from the algorithm designer’s point of view, is based on five dimensions: inducer, combiner, diversity, size, and members’ dependency. We also propose several selection criteria, presented from the practitioner’s point of view, for choosing the most suitable ensemble method.     

Structural optimization with member dimensional imperfections

The paper deals with the effect of dimensional imperfections of truss members on the minimum weight design of a structure. It is assumed that for each element its imperfections cannot exceed given a priori maximum values, called tolerances. The incorporation of the considered imperfections into the design is achieved by diminishing the limit values of state functions by the product of assumed imperfections and appropriate sensitivities. Therefore, the given method allows the introduction of tolerances into the design in a relatively simple way. In the submitted paper both members’ cross-section and length imperfections are discussed. The paper is illustrated with several design examples, considering cases with multiple loading conditions and buckling analysis. The achieved optimum solutions for designs with admissible tolerances show significant differences in structural weight and material distribution compared to ideal structures (i.e. having nominal dimensions). The calculations for designs with buckling analysis also reveal changes in material distribution compared to the designs without buckling constraints.     

Internal and contextual factors, knowledge processes and performance: From the Chinese provider’s perspective

This paper explores the influences of two internal factors, i.e. supplier team’s IT-based skills and communication with client’s team, and two contextual factors, i.e. supplier team’s understanding of client’s culture and collaboration with client’s team, on knowledge processes and performance in global sourcing of IT services from the Chinese provider’s perspective. Knowledge processes are characterized by knowledge sharing, knowledge-based coordination and expertise management, and performance is measured by product success and personal satisfaction. Data have been collected in 13 companies in Xi’an Software Park, with 26 in-depth, semi-structured interviews held with top and middle managers, and 200 structured questionnaires distributed to knowledge workers who are involved in global sourcing projects. The results indicate that supplier team’s IT-based skills, communication with client’s team, cultural understanding of client’s culture and collaboration with client’s team are positively associated with knowledge process and performance. Also, knowledge sharing, knowledge-based coordination and expertise management are found to be crucial for those influential factors to function positively and contribute to the performance. The findings of this study suggest that the effects of key factors on knowledge processes and performance in global sourcing of IT services appear to transcend the social and cultural differences; however, contextual factors seem to have more significant influences on knowledge processes and performance in global sourcing of IT services.     

Consumer decision support systems: Internet versus in-store application

This paper reports a study of consumer decision support in the context of Internet and in-store applications. A sample (n = 30) of experienced runners made running shoe selections in either ‘product only’, ‘decision support system only’, or ‘decision support system and product’ conditions. Participants’ decisions tended to be more uniform and of better quality when the DSS was available. Decision making was clearly influenced by DSS recommendations, but these were not always accepted. In this latter circumstance participants reported themselves to be relatively less happy with and less confident in their decision. Consistent with previous literature, abstract attributes were considered more frequently and given higher weightings when using the decision support system. However, predicted differences between conditions with respect to the types of attributes considered and the importance ascribed to different types of attributes were not found.     

The dynamic team forming problem: Throughput and delay for unbiased policies

The dynamic team forming problem (DTFP) for a heterogeneous group of robots is described as follows. Each robot is capable of providing a specific service. Tasks arrive sequentially over time, assume random locations in the environment, and require several different services. A task is completed when a team of robots travels to the task location and provides the required services. The goal is to minimize the expected delay between a task’s arrival and its completion. We restrict our attention to unbiased policies for the DTFP, i.e., policies for which the expected delay is the same for all tasks. We introduce three intuitive policies, and in certain asymptotic regimes we analyze their delay as a function of the arrival rate of tasks (or throughput). For each policy we show that there is a broad class of system parameters for which the policy’s performance is within a constant factor of the optimal.     

Towards understanding the relationship between team climate and software quality—a quasi-experimental study

This paper describes an empirical study that examined the work climate within software development teams. The question was whether the team climate in software developer teams has any relation to software product quality. We define team climate as the shared perceptions of the team’s work procedures and practices. The team climate factors examined were West and Anderson’s participative safety, support for innovation, team vision and task orientation. These four factors were measured before the project using the Team Selection Inventory (TSI) test to establish subject climate preferences, as well as during and after the project using the Team Climate Inventory (TCI) test, which establishes the subject’s perceptions of the climate. In this quasi-experimental study, data were collected from a sample of 35 three-member developer teams in an academic setting. These teams were formed at random and their members were blind to the quasi-experimental conditions and hypotheses. All teams used an adaptation of extreme programming (XP) to the students’ environment to develop the same software system. We found that high team vision preferences and high participative safety perceptions of the team were significantly related to better software. Additionally, the results show that there is a positive relationship between the categorization of better than preferred, as preferred and worse than preferred climate and software quality for two of the teamwork climate factors: participative safety and team vision. So it seems important to track team climate in an organization and team as one (of many) indicators of the quality of the software to be delivered.

Business Software Alliance: Industry protector or bully?

The Business Software Alliance (BSA) comprises a number of leading software companies including Adobe Systems, Autodesk, Lotus, Microsoft, Novell and the Santa Cruz Operation. The BSA has been actively pursuing corporate overuse of its members' software in the UK since 1991, and has achieved many high profile successes against leading companies. Using Anton Piller Search and Seize Orders, representatives of the BSA and their lawyers arrive unannounced at the premises concerned and carry out a diligent search of the hard disks of all the company's PCs. If the search reveals unlicensed copies of software on the hard disks (and this is usually identified by the serial numbers of the software), the infringers have generally been required to pay damages and costs and, in some cases, make abject public apologies.     

Workflow simulation for operational decision support

Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for operational decision making and continuous improvement. Here we describe a simulation system for operational decision support in the context of workflow management. To do this we exploit not only the workflow’s design, but also use logged data describing the system’s observed historic behavior, and incorporate information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM.     

Business intelligence approach to supporting strategy-making of ISP service management

The recent deregulation of telecommunication industry by the Taiwanese government has brought about the acute competition for Internet Service Providers (ISP). Taiwan’s ISP industry is characterized by the heavy pressure for raising revenue after hefty capital investments of last decade and the lack of knowledge to develop competitive strategies. To attract subscribers, all ISP dealers are making an all-out effort to improve their service management. This study proposes a Business Intelligence process for ISP dealers in Taiwan to assist management in developing effective service management strategies. We explore the customers’ usage characteristics and preference knowledge through applying the attribute-oriented induction (AOI) method on IP traffic data of users. Using the self-organizing map (SOM) method, we are able to divide customers into clusters with different usage behavior patterns. We then apply RFM modeling to calibrate customers’ value of each cluster, which will enable the management to develop direct and effective marketing strategies. For network resource management, this research mines the facility utilization over various administrative districts of the region, which could assist management in planning for effective network facilities investment. With actual data from one major ISP, we develop a BI decision support system with visual presentation, which is well received by its management staff.