Decision Processes: A Core Business Activity Supported by Information Systems

Decision-making is required in organizations on a daily basis. The decisions that are made can deal with sales, operations, finances, products, competition, employees and a whole host of other topics. Decisions can be short term, long term or emergency in nature. Organizational decisions can model or simulate the future while analyzing past or present performance. Technological advances in information systems provide large amounts of data to support decision-making as well as tools to analyze data in the context of specific problems and 'what-if' scenarios. Every organization has decision processes and protocols. Decisions are impacted by the quality of the decision-making process. The flexibility and inclusiveness of decision processes contribute to the effectiveness and usability of decisions as does the availability of high performance decision support systems. Organizations can use information systems to analyze and predict. Other critical factors include balancing objective and subjective data and collaboration among decision-makers. Decision-makers may use intuition and internal know-how along with performance data to arrive at a decision. There are many ways to use information and decision-making tools. Addressing the need for high quality decisions may cause organizations to make significant investments in information technology that can guide, support and improve internal decision-making processes.

Keywords Clinical Decision Support Systems; Collaboration; Creativity; Decision-Making; Decision Processes; Decision Support Systems; Group Decision Support Systems

Operations & Information Systems > Decision Processes: A Core Business Activity Supported by Information Systems

Overview

It is important that organizations make good decisions. Decisions guide the organization to failure or success. Therefore, the organization needs to have a process in place that increases its chances of making good decisions. Decision activity in organizations can be complex and involved; answers to problems may not be simple or straightforward and may involve many different decision-makers. The organizational decision process is more complex than an individual decision because many types of data must be gathered from different sources and given to multiple decision makers.

Organizations can make good decisions by:

• Recognizing when routine, traditional practices of decision making need to be replaced with creative decision processes
• Encouraging collaboration rather than competitiveness in decision making
• Being adaptable in decision-making to better react to an ever-changing world
• Understanding how certain types of information systems (called decision support systems) can synthesize expertise in the field to assist decision makers. Decision support systems can guide, support and improve decision making.

Decision processes are the steps organizations go through to arrive at decisions. These processes vary by size and type of organization. Good decision making means working together with others for creative solutions. Roszkiewicz (2007, p. 13) says collaboration "refers to a rich and interactive relationship similar to the type we experience when brainstorming." An orderly decision process requires that decision makers work together or collaborate on ideas about what information to consider, how solutions to problems look and the decision process itself.

But how do we get rich, interactive relationships? Individual decision-makers may lack strong skills in collaboration and may view decision-making with others as competitive instead of collaborative. One answer may be decision support systems (DSS). Information systems that support decision making are called decision support systems. According to French and Turoff (2007, p.39) decision support systems (DSS) can be used with individuals or groups to assist decision-makers by "organizing and communicating results" so each collaborative team member can come to a "shared understanding." The shared understanding can be of the problem, strategy, solution or in retrospect when viewing a project that is completed.

Organizations need to make numerous decisions to survive. Decisions can be daily shifts directing operational activity, responses to problems or far-reaching and extensive charting of strategic directions. One by-product of having to make so many decisions is that decisions may be made based upon a certain history of success in creating solutions or because of habits and perceptions held by decision-makers. The process of making decisions this way results in a routine or traditional organizational practice which may not lead to a correct result. This means that the decision-making process becomes a traditional organizational practice, instead of a creative one. Once the decision process is set in an organization, it may be used over and over again even if it doesn't fit the current problem type. Frederickson (2006) notes that organizations can be more attached to traditional practices for decision processes and internal data than to industry best practices. He suggests that organizations work to incorporate industry best practices into decision processes for the greatest value and optimal decision-making results.

Organizations need to be adaptable in how they make decisions. Brown, Steyvers, & Hemmer (2007) found that decision-makers need to adjust the methods used to make decisions because the environment in which decisions are made in organizations is constantly changing. Therefore, a decision process that worked for a particular problem at a particular time may not be as effective under other conditions. Similarly, circumstances and conditions may change so rapidly and dramatically that any former decision processes are outdated.

Obviously, an organization's goal is to be profitable and productive. We have already discussed that organizations must sometimes break from the mode of traditional set decision-making for the sake of creativity, but creativity in decision-making must also be managed. The greater the innovation and creativity, the greater value that can be added to products and services that organizations produce. On the other hand, unmanaged creativity and innovation may interfere with decision-making processes and process management because both can interrupt the logical and orderly flow that would occur with traditional, rational decision-making. Elbanna and Child (2007) suggest a connection in research of strategic decision processes between the size of the organization and the likelihood of using traditional, rational processes instead of creative ones. Such research indicates that the larger the organization, the more likely that traditional decision-making will be a set practice.

Organizations also need to closely watch whether their decision makers use logic to make decisions or whether politics plays more of a role. Rajagopalan, Rasheed, & Datta (1993, p. 351) stated that decision processes in organizations can model two types. The first is a rational type where strategic decisions are based on logic. The second is a political or behavioral type that is based on "bargaining and negotiations among individuals and organizational subunits with conflicting perceptions, personal stakes and unequal power." Faulty decisions can occur simply because an individual or group has an unequal percentage yet persuasive amount of power. The organization needs to minimize the role of politics.

The Role of Decision Makers & Decision Support System Impact

Decision-makers assume several roles in the decision making process including analyzing information, making assumptions about the validity of information and setting the parameters in 'what-if' scenarios. Decision-making may put the decision-maker in the role of change agent and require management of expectations from those not involved in decision-making. The decision-maker must be cognizant of the need to include others while balancing external input. In addition, they must also perform the role of expert communicator with other decision-makers and to those outside of the decision-making team. Decision-makers must put results of decisions into context so that the information can be effectively used as input for other decisions and to re-evaluate and modify organizational decision processes.

Today's decision makers must have a global view when it comes to decision making. Stolarczyk (2007) notes the importance of decision-makers having a global view of the world, especially in manufacturing where organizations are likely to deal with suppliers from around the globe. The ability to use information technology related to global supply chains helps companies make "crucial and timely financial decisions" (Stolarczyk, 2007, p. 52).

The decision-maker may be placed in the unenviable role of determining whether a company has a future or not. Crucial decision-making can be supported by effective DSS. Stolarczyk quotes a supply chain and logistics report on Fortune 500 companies as stating that many companies feel the supply chain technology being used doesn't adequately support decision-making in "budgeting and cash flow planning." Some barriers to acquiring adequate systems have been the lack of focus on the global supply chain and the investment cost, according to Stolarczyk.

Kelly, Hutchins, & Morrison, (n.d., abstract) et al conducted an experiment to determine the value of DSS in tactical decision-making. They found that throughout the decision making process, DSS helped guide, support and improve decision making. The experiment used experienced decision-makers engaged in real-life scenarios and found that those using DSS had less communication about simple situational information because of the ability to get the same view of the situation from the DSS. DSS were able to get to the central issues faster and formulate what action was needed. Problems noted by Kelly et al. (p.1) were human issues related to the decision-makers' ability to remember information, difficulty in getting the group centered on the same information at the right time and biases among the decision-makers.

DSS was found to assist decision makers regarding decision bias problems. Decision bias problems include decision-makers continuing to hold to a specific opinion about risk or threat even when presented with new information that contradicted the decision-makers initial opinion (Kelly et al., p. 2). Decision-makers were also found to hold on to information that was not related to the situation in making judgments. The study showed that early in the scenario and through the middle of the scenario, decision-makers supported by DSS outperformed their counterparts without DSS in identifying critical issues of "greatest tactical interest." However, late in the scenario, decision-makers without the benefit of a DSS outperformed their counterparts in identifying critical issues (Kelly et al., p. 3). The authors postulated that these results indicate that the benefits of using a DSS wear off late in a scenario because critical issues are somewhat obvious by that time even without using a DSS. Early identification of the critical factors by the DSS group could also be due to the human tendency to stick to initial information as correct.

The study by Kelly et al. showed that the group using the DSS was less likely to react and suggest tactical action against threats based on ambiguous information. The authors felt that this was due to the ability of the DSS to help the group filter and interpret ambiguous information to a higher degree than the group without the DSS. When analyzing specific performance against tactical threats, the study found that the group using the DSS was better able to defend against these tactical threats than the group not using the DSS. This could be the result of team communication, in that the group using the DSS had a greater shared understanding of the initial threat information.

DSS was also found to be helpful in terms of communication. Kelly et al. (p.4), wanted to determine whether or not using a DSS affected how decisions are made about what action to take and when. The experiment analyzed the amount of verbal communication among the decision-making teams and found that fewer communications were initiated by the DSS team among themselves but slightly more when encountering external information. Kelly et al. examined the content of verbal communications to determine whether or not it was affected by a DSS. The areas of content were coded in the following way:

• Information

• Status

• Clarification
• Assessment
• Orders or commands for action

The Kelly study concluded that with a DSS, the group was able to spend more time on clarifying, assessment and giving orders and less time on exchanging basic information and status because the DSS provided that information. The study concluded that having a DSS caused teams to communicate less frequently but more efficiently, and greater experience with a DSS could affect performance and communication even more. The authors also found that to have the optimum effect, a DSS would have to have specific features to support performance and communication such as being easy to access, having a user friendly interface and the ability to track important tactical data easily.

Using Decision Support Systems

What is a decision support system? One definition from French and Turoff (2007, p. 39) is that decision support systems (DSS) support "operational, tactical or strategic decision-making." The authors suggest that DSS must have the flexibility to make assumptions or allow for decision-maker input. It is important that DSS meet the following important criteria: 1) fit technology to purpose; 2) take a knowledge management perspective and allow collaboration; 3) data quality; 4) audit.

Each of these criteria has a specific meaning, fitting the technology to the purpose means not just the problem at hand but also organizational objectives, management processes and culture. In addition, the organization must be willing to change the technology when another type of technology or technology tool is better suited for the scenario. A knowledge management perspective means considering all the knowledge that is available in the organization that might impact decisions. Collaboration among groups and various organizational units ensures a thorough problem perspective exists. Data quality criteria address the fact that while data exists it may not be 'clean' or verified. In addition, uncertainty can exist because data from various sources may contradict data from other sources. Decision-makers are then put in the position to make judgments even with the uncertainty present in the available information. The audit criteria recognizes that all organizations audit activities in some way and decision support systems should participate by recording and monitoring information and prompting specific activity when required (French & Turoff, p.40). The audit data can also provide management teams with "shared meaning" of events and activities and make sure that actions are thoroughly analyzed after the occurrence so that the information can be input into future decision-making activities.

DSS assists in real-world decision making. In one study, DSS helps to predict change in the future. Kirilenko, Chivoiu, Crick, Ross-Davis, Schaaf, Shao, Singhania, & Swihart (2007) discuss an Internet based DSS for forest landowners whose purpose is to help in the management of family forests. The DSS provides useful information on various species, tree density and size as well as specifics to geographic region and wildlife in the region. A unique modeling capability of the tool is the ability to predict "forest dynamics" "40 years into the future" (Kirilenko et al., abstract). Important details available to users of the tool include the ability to measure and predict changes to the forest in many ways using a "Forest Vegetation Simulator." Since knowledge about forests is highly specialized, a tool of this nature is a requirement to effectively manage a forest and to have social consideration for natural habitats that cannot be easily replaced if destroyed. Unlike the dairy production DSS (Higgins, 2007) where farmers had high levels of sophisticated knowledge and history in the industry, the trend in family forest management is that new owners typically have urban backgrounds and little knowledge of forestry. The forest landowner DSS seeks to connect these new owners with the resources available through the government in the natural resources arena.

DSS can assist in problems that occur when people work in groups. Group Decision Support Systems (GDSS) have the goal of getting past the typical barriers found in group meetings and collaborative situations (Roszkiewicz). Since people are the most important and expensive resource in an organization, investments may be made in systems that corral the capabilities of people which Roszkiewicz (p.13) calls integrating "computer power in our attempt to identify, collect, organize and interpret the thoughts of the most important human resources each company has."

Challenges arising when working in groups include distributing information and hoping that the group understands the significance of the information, and organizing it and applying that information to the problems within the organization. At the same time, the inability to focus on what is important, personality conflicts and personal biases may make it difficult for the group to stick to an agenda and move the agenda forward. GDSS provide discipline and structure to group interactions while helping provide consensus regardless of the social skills of the participants. (Roszkiewicz, 2007). The goal is to capitalize on the value of the intellectual capital of the group without the downside of ineffective interactions. Roszkiewicz (2007) notes that GDSS have the ability to:

• Brainstorm
• Organize
• Prioritize

GDSS pairs web-conferencing capabilities with Internet browser based GDSS to capture free flowing comments while providing "tools for decision-making and reporting." GDSS also allows for the pre-planning of collaboration to ensure that decision processes are mapped to activities.

While decision-making can include everyday and mundane organizational decisions, the process can also include life and death situations. Physicians frequently make life and death decisions and therefore it is critical that there is a decision process in place to help physicians make good decisions. This type of challenging decision-making can be assisted by information systems because such systems can easily synthesize expert opinions for physicians. According to Lamont (2007) decision support systems can be used to ensure correct patient diagnosis. Clinical Decision Support Systems (CDSS) can make physicians aware of the latest information on an illness and also limit the likelihood of misdiagnosis of an illness. Physicians use decision support systems to help them with diagnosing illnesses and correctly interpreting symptoms. The goal of decision support systems in healthcare is to improve the safety of the patient and reduce cost. The better the information available to clinical practitioners, the higher the level of patient care. Developing a CDSS can be quite involved because it requires building a repository of diseases as well as the treatment protocols and drugs used.

The benefit of technology is that a CDSS can electronically store information from the oldest medical texts while including up-to-date information from journals (Lamont, 2007). CDSS allows medical professionals to input patient symptoms and will output possible diagnoses and links to the latest research. Newer CDSS have emphasized decreasing the amount of information that has to be entered about a patient's condition and have simplified the method for getting answers. Now, physicians can combine CDSS with electronic medical records (EMR) to coordinate and integrate information about the patient and the illness in order to improve the quality and speed of care. Integration of these various systems is supported by service oriented architecture (SOA) in which the architecture of systems is oriented towards the processes and uses by end users and is not dependent upon the underlying technology platform. SOA frees users to view information such as patient data through a similar interface no matter where it originated (Lamont, 2007). Without these systems, physicians might rely only on their previous knowledge and education. CDSS allows decision-makers to be flexible in their approach to decision making, so it is possible to include the best ideas.

The practice of using information systems to assist with decision-making has been introduced successfully to the dairy industry. As in the medical industry, synthesizing expert knowledge allows for better decision making. Higgins (2007) notes that effective decision support systems rely heavily on the knowledge of experts in the field and must be based on that knowledge. A decision support system was created for the dairy industry and the design was developed with input from dairy farmers to ensure the value of the farmers' knowledge was included to increase the value of the DSS. Higgins considers the science and technology arguments on who controls design and how system design is heavily skewed towards the designer and not necessarily end users or subject matter experts. The use of decision support systems in the dairy industry has been shown to improve productivity and reduce costs. The value of DSS in dairy production has become increasingly important with increased global competition in farm production (Higgins, 2007).

Viewpoint

Creativity & Decision-Making

The process of decision-making can be tedious or exciting depending on the outlook and tolerance of the individual, the complexity of the problem, the available support tools and the ease of collaborating with others in decision-making. A certain amount of creativity is required as problems and issues may not take the same shape or format and a variety of skills and abilities may be called upon to make successful decisions. This is especially true of decisions that involve performance and prediction. Former levels of performance may not meet current needs and the ability to accurately predict the future may depend on many factors including those over which the decision-maker has no control. Marakas and Elam (2007) call creativity "one of the most vague, ambitious and confusing terms." Matching creativity in a business environment that is accustomed to strict and accurate performance measures is a difficult task. Even if an organization has been successful in creative problem-solving and decision-making, reproducing what is difficult to quantify may seem like an impossible undertaking.

Weber (1986) suggested that decision support systems "should be designed to stimulate learning and creativity." Weber felt that the research supported a responsibility being placed on the designer of a DSS to stimulate the problem-solving and decision-making creativity of the user. Learning and growth can take place in the process of making decisions and solving problems, and a useful DSS supports both. Designers have to be exposed to users and subject matter experts in order to have a full understanding of what design issues are pertinent in making DSS usable and effective. Higgins (2007) describes a two-day workshop for dairy farmers used to examine farmers' "preexisting knowledge" as well as to determine whether or not users (farmers) felt the DSS had benefits and could be used in daily production.

Weber (1986) noted that DSS are by nature supportive of "human cognitive processes and semi-structured situations." He states that the benefit of DSS is the ability to support individual and organizational learning and performance while reducing the "cognitive differences" that people may have. Kelly et al. also found that DSS are useful in reducing the differences between the interpretations of data by decision-makers. Weber saw the shift in DSS evolution in a way that provides decision-makers with learning tools that are more effective in 'ill-structured" problem-solving scenarios. Weber (1986) defined cognitive processes as consisting of "sensation and perception" while learning is comprised of four sub-processes:

• Selection
• Construction
• Integration
• Acquisition

Further, Weber considered problem-solving to be "the active manipulation of perceived, learned, and remembered information." The difficulty in solving a problem is directly related to "the amount of structure," "the power of the chosen solution methods and the knowledge available to the user." "Cognitive strategies" are used to "acquire, store, retrieve and manipulate information" and "affect how creatively we think…" Weber calls creativity "originality in problem solving" and "an outcome of learning."

Weber noted that previous DSS were product oriented instead of process oriented and felt that in order to make DSS "systems to think with (STW)" the DSS must map to the natural cognitive processes decision-makers use in problem-solving. He suggests that designers are most successful when using analytical tools that model situations using analogy and "familiar electronic metaphors."

Marakas and Elam (1997) explored creativity in the context of the process used by the decision-maker, the tool or the DSS used to deliver the process. The findings of this study indicated that the best result to maximize creativity was combining DSS capabilities to guide the decision-making process with a user armed with knowledge and understanding of the decision-making process. This method was found to be superior in enhancing creativity over simply using a DSS or the user using the decision process without a DSS. The need to explore creativity is driven by the fact that more unique solutions are required more quickly and there is a human tendency to continue to use the same processes for finding solutions.

Balancing Intuition (Gut-Feel) & Objective Data

Decision-makers have to balance what they know with what they feel and historical organizational experience. Decision support systems cannot be designed in a vacuum and must take into account the decision processes of the organization, best practices in decision-making, the ability for an organization to learn and the system's ability to capture and ignite the creativity of the decision-maker. Studies have shown that decision-making performance is supported or hampered by:

• The decision-making process employed
• The quality of support tools
• The ability to incorporate creativity into the process and tools, and
• The effectiveness of decision-makers to collaborate with others.

Part of the balance needed in decision-making is between raw data and the decision-makers' ability to interpret meaning from the data and the real-world scenario that requires a solution. Decision-makers today have the benefit of large quantities of information and DSS can offer tools to screen, filter and contextualize data in light of problem-solving scenarios. However, decision-makers must guard against human tendencies and biases that restrict decision-making while allowing intuition to guide the creative process of decision-making. While learning can be a natural outcome of decision-making and even using DSS, decision-makers may be reluctant to shift decision processes or experiment creatively on critical decisions. Decision-makers are likely to feel that there is less risk in deploying pre-existing decision processes than new and creative ones.

Fowler (1979) suggested that information systems fail to reach their goal of "informing managers" because of being based on the assumption that managers can understand and define what information needs to exist before making decisions. Part of the intuitive nature of decision-making rests with systems designers as well as with decision-makers who anticipate the needs for data and customize the organizational decision processes to support and accelerate decision-making. Fowler stated that design strategy is important to avoid creating DSS that only encompass the designer's viewpoint and end up providing the decision-maker with answers that are "irrelevant" or "unasked.

In studying strategic decision processes of organizations, Rajagopalan et al. (1993) found several factors influencing the decision process models used including:

• Organizational context affects decision-making and is influenced by uncertainty and complexity.
• Organizational internal power structure, past performance and strategies exert substantial influence on decision processes and may follow industry patterns.
• Decision processes vary within an organization based on urgency, reason for decision, uncertainty about outcomes and the degree of resource commitment.

These findings underscore the importance of the process as well as the DSS deployed for decision-making. Decision outcomes such as timeliness, speed and quality are easily tied to the quality of a decision support system while outcomes such as organizational commitment and learning are not as easily incorporated into system design. Similarly, decision processes can be connected to organizational commitment and learning without as tangible a connection to timeliness, speed and quality. Organizations that are serious about improving the results of decision-making must work diligently to examine decision processes and incorporate desired processes into the design of decision support systems.

Terms & Concepts

Collaboration: The concept of groups of people, often with different backgrounds and skill sets, working cohesively on problem solving.

Creativity: A mental process of generating new ideas and approaches.

Decision Processes: Steps taken in the decision making process, usually requiring gathering and analysis of information related to specific decision areas.

Decision Support Systems: Components that support decision-making for specific problems. The components include people, software, databases, policies and procedures, and devices. Decision support systems are designed to increase decision making effectiveness.

Group Decision Support Systems: Systems supporting group collaboration on complex problems.

Information Systems: Interrelated components working together to collect, process, store and disseminate information to support decision making, coordination, control, analysis, and visualization in an organization (Laudon & Laudon, 2001).

Process: Steps taken to achieve some end.

Service Oriented Architecture (SOA): Service-orientation describes an architecture that uses loosely coupled services to support the requirements of business processes and users. Resources on a network in an SOA environment are made available as independent services that can be accessed without knowledge of their underlying platform implementation (Wikipedia, 2007).

Bibliography

Brown, S., Steyvers, M., Hemmer, P. (2007). Modeling experimentally induced strategy shifts. Psychological Science, 18, 40-45. Retrieved May 18, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=24281763&site=ehost-live

Dong, C., & Srinivasan, A. (2013). Agent-enabled service-oriented decision support systems. Decision Support Systems, 55, 364-373. Retrieved November 15, 2013, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=87616718&site=ehost-live

Elbanna, S. & Child, J. (2007). The influence of decision, environmental and firm characteristics on the rationality of strategic decision-making. Journal of Management Studies, 44, 561-591. Retrieved May 19, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=24892407&site=ehost-live

Fowler, F. P., Jr. (1979). The executive intelligence system as a design strategy. MIS Quarterly, 3, 21-29. Retrieved May 18, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=4679103&site=ehost-live

Frederickson, H.G. (2006). Reconsidering best practices. PA Times, 29, 11. Retrieved May 18, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=23496700&site=ehost-live

French, S. & Turoff, M. (2007). Decision support systems. Communications of the ACM, 50 , 39-40. Retrieved May 18, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=24209666&site=ehost-live

Higgins, V. (2007). Performing users: the case of a computer-based dairy decision-support system. Science, Technology & Human Values, 32, 263-286. Retrieved May 18, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=24768040&site=ehost-live

Hughes, G. D. (2003). Add creativity to your decision processes. Journal for Quality & Participation, 26, 4-13. Retrieved May 18, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=10295448&site=ehost-live

Kelly, R. T., Hutchins, S.G., & Morrison, J.G. (n.d.). Decision processes and team communications with a decision support system. [Whitepaper]. Retrieved May 18, 2007, from http://www.pacific-science.com/kmds/c2%5fconf.pdf

Kirilenko, A., Chivoiu, B., Crick, J., Ross-Davis, A., Schaaf, K., Shao, G., Singhania, V., Swihart, R. (2007). An Internet-based decision support tool for non-industrial private forest landowners. Environmental Modelling & Software, 22, 1498-1508. Retrieved May 18, 2007, from EBSCO Online Database Academic Source Premier. http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=24711682&site=ehost-live

Lamont, J. (2007). Decision support systems prove vital to healthcare. KMWorld, 16, 10-12. Retrieved May 18, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=23898467&site=ehost-live

Laudon, K.C. & Laudon, J.P. (2001). Essentials of management information systems: Organization and technology in the networked enterprise. Upper Saddle River, NJ: Prentice-Hall, Inc.

Makris, S.S., & Chryssolouris, G.G. (2013). Web-services-based supply-chain-control logic: an automotive case study. International Journal of Production Research, 51, 2077-2091. Retrieved November 15, 2013, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=85286024&site=ehost-live

Marakas, G. M. (1997). Creativity enhancement in problem solving: Through software or process. Management Science, 43, 1136-1146. Retrieved May 18, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=9709102751&site=ehost-live

Rajagopalan, N., Rasheed, A.M., and Datta, D. (1993). Strategic decision processes: Critical review and future directions. Journal of Management, 19, 349-384. Retrieved May 18, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=5711263&site=ehost-live

Riabacke, A., Larsson, A., & Danielson, M. (2011). Business intelligence as decision support in business processes: An empirical investigation. Proceedings of the European Conference On Information Management & Evaluation, 384-392. Retrieved November 15, 2013, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=60168261&site=ehost-live

Roszkiewicz, R. (2007). GDSS: The future of online meetings and true digital collaboration? The Seybold Report, 7, 13-17. Retrieved May 18, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=23835707&site=ehost-live

Stolarczyk, M. J. (2007). The IT factor. The Journal of Commerce, 8, 52. Retrieved May 18, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=24605140&site=ehost-live

Weber, E. S. (1986). Systems to think with: A response to a vision for decision support systems. Journal of Management Information Systems, 2, 85-97. Retrieved May 18, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=5745637&site=ehost-live

Wikipedia. (2007). Service Oriented Architecture (SOA). [Definition]. Retrieved May 18, 2007, from http://en.wikipedia.org/wiki/Service%5foriented%5farchitecture#SOA%5fdefinitions

Suggested Reading

IBM white paper: Enhance quality of care and clinical decision making in a security-rich environment. (2005). Retrieved May 18, 2007, from TechRepublic. http://whitepapers.techrepublic.com.com/whitepaper.aspx?docid=292536

Akella, J., Kanakamedala, K. & Roberts, R. (2007, January 4). Efficient IT shops: Two trends foreseen. CIO Magazine. Retrieved May 18, 2007, from CIO Magazine Online. http://www.cio.com/article/27979/Efficient%5fIT%5fShops%5fTwo%5fTrends%5fForeseen/2

Davenport, T. H. & Harris, J. G. (2005). Automated decision making comes of age. MIT Sloan Management Review, 46, 83-89.

Worley, C. G. & Lawler, E. E. III (2006). Designing organizations that are built to change. MIT Sloan Management Review, 48, 19- 23.