Kemal Kilic

Organizational Support for Intrapreneurship and its Interaction with Human Capital To Enhance Innovative Performance

Purpose – The main purpose of this paper is to investigate the direct and interactive effects of organizational support and human capital on the innovative performance of companies. Individual effects of the organizational support dimensions, namely: management support for generating and developing new business ideas, allocation of free time, convenient organizational structures concerning, in particular, decentralization level or decision-making autonomy, appropriate use of incentives and rewards, and tolerance for trial-and-errors or failures in cases of creative undertakings or risky project implementations, are also to be investigated.Design/Methodology/Approach – The study develops and tests a theoretical research model where the organizational support dimensions are the independent variables, innovative performance is the dependent variable, and the human capital has a moderating role in this relationship, via a questionnaire study covering 184 manufacturing firms in Turkey.Findings – Among the individual direct effects of the dimensions of organizational support, management support for idea development and tolerance for risk taking are found to exert positive effects on innovative performance. Availability of a performance based reward system and free time have no impact on innovativeness, while work discretion has a negative one. As for the role of human capital (HC), it is found to be an important driver of innovative performance especially when the OS is limited. However, when the levels of both HC and OS are high, innovative performance does not increase any further.Originality/Value – Two distinct research streams, namely organizational support literature and human capital literature, have already focused on their individual impacts on the innovative performance. However, a combination of these separate streams was not tried before. The paper discusses and investigates what will happen when both positive drivers interact with each other. Moreover, it also investigates how organizational support and human capital are complementary.

Fuzzy system modeling in pharmacology: an improved algorithm

In this paper, we propose an improved fuzzy system modeling algorithm to address some of the limitations of the existing approaches identified during our modeling with pharmacological data. This algorithm differs from the existing ones in its approach to the cluster validity problem (i.e., number of clusters), the projection schema (i.e., input membership assignment and rule determination), and significant input determination. The new algorithm is compared with the Bazoon-Turksen model, which is based on the well-known Sugeno-Yasukawa approach. The comparison was made in terms of predictive performance using two different data sets. The first comparison was with a two variable nonlinear function prediction problem and the second comparison was with a clinical pharmacokinetic modeling problem. It is shown that the proposed algorithm provides more precise predictions. Determining the degree of significance for each input variable, allows the user to distinguish their relative importance.

Pharmacokinetic application of fuzzy structure identification and reasoning

The aim of this paper is to introduce the algorithm proposed in [Fuzzy Sets and System, 2002] and some further modifications. Its applications presented in [ibid: A comparison of five approaches for lithium dose and serum concentration prediction, IFSA-NAFIPS 2001, pp. 104-110] is reviewed as a full collection of its use in pharmacokinetic analysis. First a recently developed fuzzy system modeling algorithm and approximate reasoning tool are introduced along with the modifications. Later the performance of the proposed algorithm is tested in two different data sets and compared with some well-known algorithms from the literature. In the comparison, individualized pharmacokinetic data (i.e., alprazolam data) and population pharmacokinetic data (i.e., lithium data) are used. The comparisons demonstrate that the proposed algorithm can be successfully applied in pharmacokinetic modeling.

A Fuzzy System Modeling Algorithm for Data Analysis and Approximate Reasoning

In this paper a new fuzzy system modeling algorithm is introduced as a data analysis and approximate reasoning tool. The performance of the proposed algorithm is tested in two different data sets and compared with some well-known algorithms from the literature. In the comparison two benchmark data sets from the literature, namely the automobile mpg (miles per gallon) prediction and Box and Jenkins gas-furnace data are used. The comparisons demonstrated that the proposed algorithm can be successfully applied in system modeling.

Modeling innovation: Determinants of innovativeness and the impact of innovation on firm performance

The objective of this paper is to report on the method, analysis, and conclusions concerning two research questions formulated as: <i>What</i> <i>are</i> <i>the</i> <i>determinants</i> <i>of</i> <i>innovation</i> <i>at</i> <i>firm</i> <i>level?rdquo</i> <i>and</i> <i>ldquowhat</i> <i>is</i> <i>the</i> <i>impact</i> <i>of</i> <i>innovation</i> <i>on</i> <i>firm</i> <i>performance?rdquo</i> The results are based on an empirical study covering 184 manufacturing firms in the Northern Marmara region within Turkey. A comprehensive and integrated innovation model is presented composed of two sub-models proposed in line with the two research questions posed. Results and conclusions are presented.

Decision support for packing in warehouses

Packing problems deal with loading of a set of items (objects) into a set of boxes (containers) in order to optimize a performance criterion under various constraints. With the advance of RFID technologies and investments in IT infrastructures companies now have access to the necessary data that can be utilized in cost reduction of packing processes. Therefore bin packing and container loading problems are becoming more popular in recent years. In this research we propose a beam search algorithm to solve a packing problem that we encountered in a real world project. The 3D-MBSBPP (Multiple Bin Sized Bin Packing Problem) that we present and solve has not been analyzed in literature before, to the best of our knowledge. We present the performance of our proposed beam search algorithm in terms of both cost and computational time in comparison to a greedy algorithm and a tree search enumeration algorithm.

A new fuzzy inference approach based on Mamdani inference using discrete type 2 fuzzy sets

Fuzzy system modeling (FSM) is one of the most prominent system modeling tools in analyzing the data in the presence of uncertainty. Linguistic fuzzy rulebase (LFR) structure, in which both the antecedent and consequent variables are represented by fuzzy sets, is the most well known fuzzy rulebase structure in the literature. The proposed FSM method identifies LFR system model by executing fuzzy C-Means (FCM) clustering method. One of the sources of uncertainty in system modeling is the uncertainty in selecting learning parameters. In order to capture this uncertainty in a more realistic way, the antecedent and consequent variables are represented by using type 2 fuzzy sets that are constructed by executing FCM method with different level of fuzziness, m, values. The proposed system modeling approach is applied on a well-known benchmark data set where the goal is to predict the price of a stock. After comparing the results with the ones obtained with other system modeling tools, it can be claimed successful results are achieved.

An interactive machine-learning-based electronic fraud and abuse detection system in healthcare insurance

The nature of problem and claim management environment are well-defined.Very comprehensive literature revive is presented.An IML based novel DSS detecting fraud and abuse in healthcare insurance is developed.Unlike earlier studies, the system embraces all relevant actors and commodities.Real data are used for the evaluation and perform better results wrt previous studies. Detecting fraudulent and abusive cases in healthcare is one of the most challenging problems for data mining studies. However, most of the existing studies have a shortage of real data for analysis and focus on a very limited version of the problem by covering only a specific actor, healthcare service, or disease. The purpose of this study is to implement and evaluate a novel framework to detect fraudulent and abusive cases independently from the actors and commodities involved in the claims and an extensible structure to introduce new fraud and abuse types. Interactive machine learning that allows incorporating expert knowledge in an unsupervised setting is utilized to detect fraud and abusive cases in healthcare. In order to increase the accuracy of the framework, several well-known methods are utilized, such as the pairwise comparison method of analytic hierarchical processing (AHP) for weighting the actors and attributes, expectation maximization (EM) for clustering similar actors, two-stage data warehousing for proactive risk calculations, visualization tools for effective analyzing, and z-score and standardization in order to calculate the risks. The experts are involved in all phases of the study and produce six different abnormal behavior types using storyboards. The proposed framework is evaluated with real-life data for six different abnormal behavior types for prescriptions by covering all relevant actors and commodities. The Area Under the Curve (AUC) values are presented for each experiment. Moreover, a cost-saving model is also presented. The developed framework, i.e., the eFAD suite, is actor- and commodity-independent, configurable (i.e., easily adaptable in the dynamic environment of fraud and abusive behaviors), and effectively handles the fragmented nature of abnormal behaviors. The proposed framework combines both proactive and retrospective analysis with an enhanced visualization tool that significantly reduces the time requirements for the fact-finding process after the eFAD detects risky claims. This system is utilized by a company to produce monthly reports that include abnormal behaviors to be evaluated by the insurance company.

A genetic algorithm based feature weighting methodology

In this paper a genetic algorithm based feature weighting methodology that is based on k-nn classifier is presented. The performance of the algorithm is evaluated in two folds. First of all, its differentiation capability among relevant and irrelevant features is evaluated. This is achieved by introducing dummy variables to a well known benchmark data set, namely the Iris Data. Secondly, its predictive performance is also evaluated. The results are encouraging in the sense that the proposed algorithm specifies lower weights to the dummy variables and yields high classification accuracy.

A novel Hybrid Genetic Local Search Algorithm for feature selection and weighting with an application in strategic decision making in innovation management

In some applications, one needs not only to determine the relevant features but also provide a preferential ordering among the set of relevant features by weights. This paper presents a novel Hybrid Genetic Local Search Algorithm (HGA) in combination with the k-nearest neighbor classifier for simultaneous feature subset selection and feature weighting, particularly for medium-sized data sets. The performance of the proposed algorithm is compared with the performance of alternative feature subset selection algorithms and classifiers through experimental analyses in the various benchmark data sets publicly available on the UCI database. The developed HGA is then applied to a data set gathered from 184 manufacturing firms in the context of innovation management. The data set consists of scores of manufacturing firms in terms of various factors that are known to influence the innovation performance of manufacturing firms and referred to as innovation determinants, and their innovation performances. HGA is used to determine the relative significance of the innovation determinants. Our results demonstrated that the developed HGA is capable of eliminating the irrelevant features and successfully assess feature weights. Moreover, our work is an example how data mining can play a role in the context of strategic management decision making.