Festus Oluseyi Oderanti

Knowledge network modelling to support decision-making for strategic intervention in IT project-oriented change management

This paper focuses on knowledge management to enhance decision support systems for strategic intervention in information technology (IT) project-oriented change management. It proposes a model of change management knowledge networks (CMKNM) to support decision by tackling three existing issues: insufficient knowledge traceability based on the relationships between knowledge elements and key factors, lack of procedural knowledge to provide adequate policies to guide changes, and lack of ‘lessons learned’ documentation in knowledge bases. A qualitative method was used to investigate issues surrounding knowledge mobilisation and knowledge networks. Empirical study was undertaken with industries to test the CMKNM. Results are presented from the empirical study on the key factors influencing knowledge mobilisation in IT project-oriented change management, knowledge networks and connections. The CMKNM model allows key knowledge mobilisation factors to be aligned with each other; it also defines the connections between knowledge networks allowing knowledge to be mobilised by tracing knowledge channels to support decision.

Application of strategic fuzzy games to wage increase negotiation and decision problems

We propose a flexible decision support scheme which could be used in managing the wage negotiation between employers and employees. This scheme uses fuzzy inference systems and game theory concepts in arriving at decisions on future wage increase which could be more mutually agreeable. For example, rather than specifying 5% yearly increase of wages, we propose that the uncertain factors which are mostly difficult to predict and that could affect wage decisions need to be taken into consideration by the wage formula. These include business revenues or (profit), inflation rate, number of competitors, cost of production, and other uncertain factors that may affect business operations. The accuracy of the fuzzy rule base and the game strategies will help to mitigate the adverse effects that a business may suffer from these uncertain factors. Based on our scheme, we propose that employers and employees should calculate their future wage by using a fuzzy rule base and strategies that take into consideration these uncertain variables. The proposed approach is illustrated with a case study and the procedure and methodology may be easily implemented by business organizations in their wage bargaining and decision processes.

Automatic fuzzy decision making system with learning for competing and connected businesses

We study uncertainties surrounding competition on business networks and board games. We investigate these uncertainties using concepts of fuzzy logic and game theory. We investigate how the payoff of the players is affected by a number of factors. These include the level of connectivity or number of links, the number of competitors, possible constraints on the networks and on the boards, as well as choice of strategy adopted by competitors. We introduce one fuzzy player in the game. This player uses fuzzy rules to make strategic decisions. We introduce learning to train and analyze how the fuzzy player adapts over time during the game.

Fuzzy inference game approach to uncertainty in business decisions and market competitions

The increasing challenges and complexity of business environments are making business decisions and operations more difficult for entrepreneurs to predict the outcomes of these processes. Therefore, we developed a decision support scheme that could be used and adapted to various business decision processes. These involve decisions that are made under uncertain situations such as business competition in the market or wage negotiation within a firm. The scheme uses game strategies and fuzzy inference concepts to effectively grasp the variables in these uncertain situations. The games are played between human and fuzzy players. The accuracy of the fuzzy rule base and the game strategies help to mitigate the adverse effects that a business may suffer from these uncertain factors. We also introduced learning which enables the fuzzy player to adapt over time. We tested this scheme in different scenarios and discover that it could be an invaluable tool in the hand of entrepreneurs that are operating under uncertain and competitive business environments.

Automated game approach to wage negotiation and decision problems

We proposed a profit sharing strategic game approach to wage negotiation and decision problems in business organisations. In the scheme, both the employer and the union choose their strategies and the game is played in five rounds. We refer to our model as automated game approach to wage negotiation and decision problems (AGAW). Our method proposes profit (positive or negative) sharing sequential game approach in modeling wage increase decisions within a firm in a competitive industry and this game is played between the firms management and the union. The proposed approach is illustrated with a case study. The procedure and methodology proposed in this research may be easily implemented by business organisations in their wage bargaining and decision processes.

Wage increment problems and fuzzy inference approach

We propose a flexible scheme for employers and employees which they can use as a decision support system in their future salary negotiations. This scheme uses a fuzzy inference system for arriving at more mutually agreeable decisions on wage negotiation. For example, rather than specifying 5% yearly increase of wages, we propose that the wage increase formula needs to take into consideration other factors which are mostly difficult to predict with certainty. These include inflation rate, business revenues or (profit), cost of production, number of competitors and other uncertain factors that may affect business operations. The accuracy of the fuzzy rule base will help to mitigate the adverse effects that a business may suffer from these uncertain factors. Based on our scheme, we propose that employers and employees should calculate their future wage by using a fuzzy rule base that takes into consideration those variables which are mostly uncertain and that could affect their decisions.

A holistic review and framework for sustainable business models for assisted living technologies and services

Despite heavy investment in health and social care sectors in the UK, large scale deployment of assisted living technologies and services (ALTS) continues to face significant obstacles, and the lack of sustainable business models (BM) is widely regarded as one of the greatest hindrances. Based on a systematic review of previous studies, this paper identifies current trends in digital technologies and how they are used in assisted living. We categorise and analyse these technologies and through the lens of diffusion of innovation theory (DOI), we review the concepts and frameworks of BM as used in the literature to suggest conceptual frameworks for sustainable BMs for scalable ALTS in digital economy. This is expected to help in reducing the pressure on the already stretched health and social care services in the UK and other similar economies. Our approach suggests that BM canvas and DOI innovation diffusion characteristics are complements, not substitutes.

Uncertainty in business decisions and fuzzy games approach

We developed a decision support scheme that could be used and adapted to various business decision processes. These involve decisions that are made under uncertain situations such as business competition in the market or wage negotiation within a firm. The scheme uses game strategies and fuzzy inference concepts to effectively grasp the variables in these uncertain situations. The games are played between human and fuzzy players. The accuracy of the fuzzy rule base and the game strategies help to mitigate the adverse effects that a business may suffer from these uncertain factors We also introduced learning which enables the fuzzy player to adapt over time. We tested this scheme in different scenarios and discover that it could be an invaluable tool in the hand of entrepreneurs that are operating under uncertain and competitive business environments.

Business process modelling and visualisation to support e-government decision making: Business/IS alignment

Alignment between business and information systems plays a vital role in the formation of dependent relationships between different departments in a government organization and the process of alignment can be improved by developing an information system (IS) according to the stakeholders’ expectations. However, establishing strong alignment in the context of the eGovernment environment can be difficult. It is widely accepted that business processes in the government environment plays a pivotal role in capturing the details of IS requirements. This paper presents a method of business process modelling through UML which can help to visualise and capture the IS requirements for the system development. A series of UML models have been developed and discussed. A case study on patient visits to a healthcare clinic in the context of eGovernment has been used to validate the models.

Fuzzy Inference Approach to Uncertainty in Budget Preparation and Execution

In recent times, diverse uncertainties in the global economic environment have made it difficult for most countries to meet their financial obligations. For example, according to statistics from European Commission, 24 out of 29 recorded European Economic Area member countries had budget deficits in 2014. Therefore through modelling and simulations, this paper proposes flexible decision support schemes that could be used in managing the uncertainties in budgeting. Rather than entirely relying on estimates of anticipated revenues (which are uncertain and difficult to predict) in government budgeting, the scheme proposes incorporating fuzzy inference systems (which is able to capture both the present and future uncertainty) in predicting the anticipated revenues and consequently, in proposing government expenditures. The accuracy of fuzzy rule base helps in mitigating adverse effects of uncertainties in budgeting. We illustrated the proposed scheme with a case study which could easily be adapted and implemented in any budgeting scenarios.