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Interest has grown in how management controls operate together as a package of interrelated mechanisms. This study aims to contribute to the topic by focusing on a single industry in one country, addressing controls in medium-sized enterprises (MEs). It explores how accounting and other forms of control commonly combine and the associations these combinations have with firm characteristics and context.

This study used a cross-sectional sample of 242 firms. Data were collected in 2015 from a survey of the Italian mechanical-engineering industry.

The MEs studied used two different control configurations. One group relatively strongly emphasized most studied controls, except for centralizing decision-making and strong hierarchy; the other relied on centralization and emphasized other controls less. Size, task programmability, outcome measurability, complexity in terms of the extensiveness of the product range and environmental unpredictability can predict the configuration in use.

No broad-based empirical evidence on control configurations in MEs currently exists. Previous research has focused on to what extent control systems affect business effectiveness or efficiency, without assessing how, and in which contexts, they combine.

The purpose of this paper is to implement a genetic algorithmic geared toward building an optimized investment portfolio exploring data set from stocks of firms listed on the Nigerian exchange market. To provide a research-driven guide toward portfolio business assessment and implementation for optimal risk-return.

The approach was to formulate the portfolio selection problem as a mathematical programming problem to optimize returns of portfolio; calculated by a Sharpe ratio. A genetic algorithm (GA) is then applied to solve the formulated model. The GA lead to an optimized portfolio, suggesting an effective asset allocation to achieve the optimized returns.

The approach enables an investor to take a calculated risk in selecting and investing in an investment portfolio best minimizes the risks and maximizes returns. The investor can make a sound investment decision based on expected returns suggested from the optimal portfolio.

The data used for the GA model building and implementation GA was limited to stock market prices. Thus, portfolio investment that which to combines another capital market instrument was used.

Investment managers can implement this GA method to solve the usual bottleneck in selecting or determining which stock to advise potential investors to invest in, and also advise on which capital sharing ratio to reduce risk and attain optimal portfolio-mix targeted at achieving an optimal return on investment.

The value proposition of this paper is due to its exhaustiveness in considering the very important measures in the selection of an optimal portfolio such as risk, liquidity ratio, returns, diversification and asset allocation.

The purpose of this paper is to analyse the propensity for innovation and co-creation in service companies in receipt of knowledge intensive business services (KIBS) type services through their intensive incorporation of knowledge.

In order to achieve this study objective, the authors first carried out a literature review in order to specify the scope of the construct; second, the authors applied a survey based on the “Spinner model”; third, the authors undertook research on KIBS clients and restricted the results to this population sample; and fourth, based on the validated and applied models deriving from the base construct, the authors presented the results obtained.

This concludes that the “Spinner” model is valid for explaining the propensity for innovation and co-creation in companies receiving KIBS. The results demonstrate evidence of innovation processes as a result of the intensive application of knowledge associated with co-creation and knowledge transfers.

This provides managers with a better understanding of the barriers that may obstruct the implementation of co-creation and knowledge transfers. Hence, the variables analysed may guide managers in defining and planning innovation strategies. Furthermore, this enables each company receiving such services to validate them and establish indices for the innovation propensity of specialist (knowledge intensive) service providers within the framework of benchmarking exercises.

This study sets out a new means of analysing the propensity for innovation and the co-creation of knowledge by companies making recourse to knowledge intensive services. From the theoretical point of view, this defines a new construct and a means of classification for companies supplying services designed for their propensity for innovation and co-creation. From the practical perspective, this study provides the companies subject to research with the opportunity to perceive their respective position in relation to other companies. In addition to this, ranking companies in such a way may drive the need to develop new knowledge for future co-creation and innovation processes.