Decision Making and Strategic Negotiation (DMSN)

Figure 1. The Positioning of the Decision Making and Strategic Negotiation Group

Decision making is at the core of business and management activities.  It is required in any management context (i.e. planning, organizing, staffing, directing, and controlling) and in any business function (e.g. operation, marketing, finance, human resource, etc.).  As shown in Figure 1, in the School of Business and Management – ITB [SBM ITB], the Decision Making and Strategic Negotiation [DMSN] expertise group focus on knowledge, skills, and research conducts on problem analysis, decision formulation, strategic negotiation, and decision communication in the business, management, and policymaking contexts.  DMSN group views the conduct of making decisions, negotiations, and communications in all management contexts (i.e. planning, organizing, staffing, directing, and controlling) and business functions (e.g. operation, marketing, finance, human resource, etc.) using a holistic (non-siloed) perspective. 

There is a gap between how the decision-making field is traditionally formulated (normative decision making) and the way people actually make decisions (descriptive decision making).  The traditional view (i.e. the hard system approach) tends to view the decision-making field from a linear perspective.  However, business and management issues are dealing with “soft” social systems where people behave autonomously according to their values and beliefs.  DMSN group utilize and synthesize both hard and soft systems approaches (see Table 1) in defining, formulizing, and solving business and management decision-making issues.  With the approach, more holistic, adaptive, flexible, and collaborative decision-making conducts can be achieved.

Table 1. Hard System and Soft System Approach in Decision Making

Hard Systems Approach

The decision-making problem and objective can be clearly defined. (often by an external stakeholder)

There is a consensus on the problem definition and the objective of the decision-making.

Top-down Planning.

People are considered as passive objects.

Future conditions are largely governed by today’s decisions.  Decision making is considered as a static process.                           

 

Soft Systems Approach

Each stakeholder can have different perspectives on the exact definition of the decision-making problem and objective.

There are different perspectives and interests on the problem definition and the objective of the decision-making.

Bottom-up Planning.

People are considered as active objects.

Future conditions are full of dynamics and uncertainty and require open-ended decision making that can be updated as new information come.

 

The systems approach is one of the specialties of the DMSN group. It is selected since DMSN is interested in decision-making issues in real-life contexts. In such context, people involved behave in a unique behavior based on their internal properties such as culture, experience, motivation, and interest. Furthermore, these situations may involve negotiation conduct. This research direction is highly relevant in Indonesia that is comprised of people with different cultural backgrounds. Together with theoretical perspective from the service science, social science, decision science, and design science fields, DMSN explore myriad of research areas with a systems perspective.  Some of the research areas are decision analysis, negotiation and confrontation analysis, value co-creation platforms, agent-based social simulations, service-dominant logic, soft system approaches, big data analytics, etc.  The research explored in DMSN is aimed to be applicable in real-life practice.  Concurrently real-life practices are the source of inspiration to enrich the existing theories/models.  The overview of DMSN perspective in viewing the research and practice mutualism is portrayed in the following figure.

Figure 2. The Overview of Decision Making and Strategic Negotiation Research Map

Together with popular analytical research methods (e.g. quantitative, qualitative,  and mixed research methods), the DMSN group also conducts research with methodologies known from the systems science domain.  As shown in Figure 3, DMSN group conduct both hard and soft systems research approaches.  Some of the systems methodologies employed in the DMSN research group are soft systems methodology, total systems intervention, agent-based modeling, systems-dynamics modeling simulations, service science, big data analytics, etc.

Figure 3. The Research Approaches in the Decision Making and Strategic Negotiation Group

There are five big areas in which members of the Decision Making and Strategic Negotiation Grup conduct their teaching, research, and community services activities.  As shown, the five areas are decision making, social simulation, negotiation, service science, and business analytics. The decision-making area involves fields such as normative decision making, systems approaches in decision making, etc. The social simulation area involves fields such as systems approaches, agent-based simulation, system dynamics simulation, discrete-event simulation, etc. The negotiation area involves fields such as game theory, the interplay of competition and cooperation, negotiation analysis, etc. The service science area involves fields such as service systems, interdisciplinary approaches in service-science, etc. The “Business Analytics” area involves fields such big-data analytics, advanced statistics, decision support systems, digital platforms for value creations, etc.  moreover these five research pillars are in line with the competencies that future businesses requires defined by the World Economic Forum such as complex problem solving, critical thinking, judgment and decision making in a VUCA (Volatile, Uncertain, Complex, Ambiguous) environment,  negotiation, etc.

Figure 4. Five Expertise Pillars of the Decision Making and Strategic Negotiation Group

Selected Publications

Mangkusubroto, K., Putro, U. S., Novani, S., & Kijima, K. (Eds.). (2016). Systems Science for Complex Policy Making: A Study of Indonesia (Vol. 3). Springer.

Putro, U. S., Kijima, K., & Takahashi, S. (2000). Adaptive learning of hypergame situations using a genetic algorithm. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans30(5), 562-572.

Putro, U. S., Novani, S., Siallagan, M., Deguchi, H., Kantani, Y., Kaneda, T., … & Tanuma, H. (2008). Searching for effective policies to prevent bird flu pandemic in Bandung city using agent‐based simulation. Systems Research and Behavioral Science25(5), 663-673.

Hermawan, P., & Kijima, K. (2009). Conflict analysis of Citarum River Basin pollution in Indonesia: A drama-theoretic model. Journal of Systems Science and Systems Engineering18(1), 16-37.

Hermawan, P., & Yoshanti, G. (2016). Unfolding the problem of Batik waste pollution in Jenes River, Surakarta, using critical system heuristics and drama-theoretic dilemma analysis. In Systems Science for Complex Policy Making (pp. 93-108). Springer, Tokyo.

Novani, S., & Kijima, K. (2012). Value co-creation by customer-to-customer communication: Social media and face-to-face for case of airline service selection. Journal of Service Science and Management5(1), 101-109.

Novani, Santi, and Kyoichi Kijima. “Efficiency and effectiveness of C2C interactions and mutual learning for value co-creation: Agent-based simulation approach.” International Journal of Business and Management 8.9 (2013): 50.

Siallagan, M., Martono, N. P., & Putro, U. S. (2017). Agent-Based Simulations of Smallholder Decision-Making in Land Use Change/Cover (LUCC) Problem. In Agent-Based Approaches in Economics and Social Complex Systems IX (pp. 97-107). Springer, Singapore.

Sunitiyoso, Y., Avineri, E., & Chatterjee, K. (2011). The effect of social interactions on travel behaviour: An exploratory study using a laboratory experiment. Transportation Research Part A: Policy and Practice45(4), 332-344.

Sunitiyoso, Y., & Matsumoto, S. (2009). Modelling a social dilemma of mode choice based on commuters’ expectations and social learning. European Journal of Operational Research193(3), 904-914.

Sunitiyoso, Y., Avineri, E., & Chatterjee, K. (2013). Dynamic modelling of travellers’ social interactions and social learning. Journal of Transport Geography31, 258-266.

Ariyanto, K. (2014). Analyzing the Conflict between Football Organizations in Indonesia. Procedia-Social and Behavioral Sciences115, 430-435.

Nuraeni, S., Arru, A. P., & Novani, S. (2015). Understanding consumer decision-making in tourism sector: conjoint analysis. Procedia-Social and Behavioral Sciences169, 312-317.

Mayangsari, L., Novani, S., & Hermawan, P. (2015). Understanding a viable value co-creation model for a sustainable entrepreneurial system: a case study of Batik Solo industrial cluster. International Journal of Entrepreneurship and Small Business26(4), 416-434.

Wasesa, M., Stam, A., & van Heck, E. (2017). The seaport service rate prediction system: Using drayage truck trajectory data to predict seaport service rates. Decision Support Systems95, 37-48.

Wasesa, M., Stam, A., & van Heck, E. (2017). Investigating agent-based inter-organizational systems and business network performance: Lessons learned from the logistics sector. Journal of Enterprise Information Management, 30(2), 226-243.

Sani, K., Siallagan, M., Putro, U. S., & Mangkusubroto, K. (2018). Indonesia energy mix modelling using system dynamics. International Journal of Sustainable Energy Planning and Management18, 29-51.

Bintoro, B. P. K., Simatupang, T. M., Putro, U. S., & Hermawan, P. (2015). Actors’ interaction in the ERP implementation literature. Business Process Management Journal21(2), 222-249.