This paper investigates the mutual interdependencies between organizational architectures, decision making and performance. Through applying agency-based Monte-Carlo simulations, we reveal how three types of organizational structure (hierarchical, polyarchical and hybrid) aggregate innovations on the micro level into corporate performance. By considering three different initial project portfolios (incremental innovations, innovations with spillover effects and innovations that have to overcome a critical mass), we analyze which organizational architectures may be superior regarding selecting good projects, avoiding collective myopia and overcoming organizational inertia. Results suggest that in a risky environment, firms with rigid hierarchies can achieve a much higher performance than horizontally organized firms even when the quality of the decision-making by managers is poor. Results also highlight the dangers involved in erecting a more hybrid-type organization because such an organization might become over-challenged and unable to handle risky innovations adequately. Finally, we discuss how firms could be structured to increase performance and to minimize risks.

Science and Technology Parks (STPs) foster innovation between firms inhabiting the cluster. Networking channels are considered as integral parts of the knowledge exchange process, and therefore the innovation process. We simulated three organizational topologies for STPs; firstly, in the star model all are connected to the cluster initiative (CI), secondly the strongly connected model, when all are connected to each other, and finally the randomly connected model, where the network follows no centralised topology. Analyses used adjacency matrixes and Monte-Carlo simulation, trading transaction (networking) costs against knowledge benefit. Results show that star topology is the most efficient form from the cost perspective, and this is especially the case for start-up STPs. Later, when the cost of knowledge transformation is lowered, then the strongly connected model becomes the most efficient topology, but this transition to high transaction costs is very risky if direct ties do not quickly result in tangible benefits.

Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect. Due to the exponential growth in malware, manual methods of malware are increasingly ineffective. Although prior writers have proposed numerous high-quality approaches, static and dynamic assessments inherently necessitate intricate procedures. The obfuscation methods used by modern malware are incredibly complex and clever. As a result, it cannot be detected using only static malware analysis. As a result, this work presents a hybrid analysis approach, partially tailored for multiple-feature data, for identifying Android malware and classifying malware families to improve Android malware detection and classification. This paper offers a hybrid method that combines static and dynamic malware analysis to give a full view of the threat. Three distinct phases make up the framework proposed in this research. Normalization and feature extraction procedures are used in the first phase of pre-processing. Both static and dynamic features undergo feature selection in the second phase. Two feature selection strategies are proposed to choose the best subset of features to use for both static and dynamic features. The third phase involves applying a newly proposed detection model to classify android apps; this model uses a neural network optimized with an improved version of HHO. Application of binary and multi-class classification is used, with binary classification for benign and malware apps and multi-class classification for detecting malware categories and families. By utilizing the features gleaned from static and dynamic malware analysis, several machine-learning methods are used for malware classification. According to the results of the experiments, the hybrid approach improves the accuracy of detection and classification of Android malware compared to the scenario when considering static and dynamic information separately.

Science and Technology Parks (STPs) are often used as tools to foster regional development. They seek innovations, innovators and encourage innovation amongst the constituent firms, including by networking and knowledge spill over between the inhabitants, Universities and sources of capital. The low success rate of STPs led us to investigate how STP architecture can best cope with a changing and challenging innovation environment, through start-up to early maturity and full maturity, in a preliminary effort to arrive at an evidence-based scheme to help avoid failure. Three different types of architecture were investigated: open (market), star (hierarchy), and closed strong (adhocracy, ambidextrous). Open (market) architecture suffered both from high transaction costs while not protecting against poor decision-making. Results show that it is very beneficial to have a central Cluster Initiative (CI) controlling the decision-making process (star, hierarchy) in the early stages of STP development, where potential gains and losses are relatively modest. However in the early maturity stage with commitment to a high-growth trajectory, a high quality of decision-making is required amongst managers and decisions are best taken by the CI with the input of optimally two individual on-cluster firms. The situation where CI is supported by goodquality decisions from on-cluster firms-an ad hoc, ambidextrous situation-is superior when good innovations abound and the STP has acquired some maturity. However, in environments with a surfeit of poor-fit innovations, this becomes a high-risk strategy with high potential losses and indeed in this situation, retaining a hierarchical (CI only) decision process is most helpful, even when the quality of decision-making amongst CI managers is poor. Results indicate that success involves attracting enough small innovative firms which-in turn-attract larger firms, whose detailed sector-relevant insight improves CI decision-making.

Business clusters are often intended to provide the environment needed to stimulate the financial growth of corporate inhabitants. However, many fails, prompting scholars to strive to identify the relevant success factors. In this paper, we identify factors promoting the growth of high-technology firms using a longitudinal dataset for both on-and off-cluster firms in Mjärdevi Science Park (MSP) at Linköping, Sweden. A panel data approach was used to investigate factors influencing the growth of on-cluster firms using off-cluster firms as a control group. Size and age influence turnover, as does the ability to innovate, but whereas size and age have a quadratic (non-linear) impact on financial growth, innovation capabilities have a positive linear impact. Employment is mainly correlated to age, previous years' innovation and shareholder investment. Innovation output, (the ratio of patents asset value to turnover) is correlated to networking measured as social expenditure, which in turn exhibits a positive influence on innovation capabilities.