The Knowledge Absorptive Capacity to Improve the Cooperation and Innovation in the Firm

Purpose : The purpose of this paper is to study the absorptive capacity types in the knowledge management literature and aims to understand how companies can strength their contexts of cooperation in order to innovate. Design/methodology/approach: A balanced panel of 1,220 firms that respond to the Survey of Business Strategies for a three-year period was used, which represents a total of 3,660 observations. Findings: The justification of absorptive capacity typology for an innovation efficiency process. The influence of the potential and realized absorptive capacity on new products is significant and causes effects on internal research and development in diverse way. The impact of the joint ventures, suppliers’ cooperation and customers’ cooperation are significant on absorptive capacity. Research limitations/implications: It would be interesting to extend the research to another innovation metrics as new organizational methods, new processes, new designs or new methods in the use of sales channels. Practical implications: The agreement of cooperation activities constitutes an important decision for the firm’s innovation. Companies must be conscious that while suppliers and customers’ cooperation are relevant cooperation actions to increase the internal research and development, joint ventures and customers’ cooperation are significant to the growth of the new products. Social implications: The types of absorptive capacity and internal research and development serve as mediating mechanisms between cooperative activities and innovative performance. Originality/value: This paper advances the literature on absorptive capacity by showing how firms use their positions of technological vigilance and management to form their capabilities, and subsequently, to enhance innovation outcomes. This study considers it is necessary to analyze the typology of the absorptive capacity that can allow managers to understand an innovation efficiency process in the cooperation context and make better decisions. The confluence of cooperation activities, absorptive capacity and organizational objectives in internal research and development obtain higher innovative results.Peer Reviewe

Assessing Knowledge Management in the Power Sector through a Connectionist Model

It has been proven that Artificial Intelligence, in general, and Artificial Neural Networks, in particular, can be successfully applied to problems in the field of Knowledge Management (KM). One such problem is the identification and assessment of a company’s KM status. Nowadays the importance of KM to organisational survival and for the maintenance of competitive strength is widely acknowledged. Several connectionist models for the assessment and analysis of KM status are proposed and applied in this work. These models account for the specific features of a company in the Energy sector/Power sector: a dynamic, essential service and one of the basic pillars that supports the so-called “welfare state”, constituting an established strategic sector in any globalized economy

DIPKIP: A connectionist Knowledge Management System to Identify Knowledge Deficits in Practical Cases

This study presents a novel, multidisciplinary research project entitled DIPKIP (data acquisition, intelligent processing, knowledge identification and proposal), which is a Knowledge Management (KM) system that profiles the KM status of a company. Qualitative data is fed into the system that allows it not only to assess the KM situation in the company in a straightforward and intuitive manner, but also to propose corrective actions to improve that situation. DIPKIP is based on four separate steps. An initial “Data Acquisition” step, in which key data is captured, is followed by an “Intelligent Processing” step, using neural projection architectures. Subsequently, the “Knowledge Identification” step catalogues the company into three categories, which define a set of possible theoretical strategic knowledge situations: knowledge deficit, partial knowledge deficit, and no knowledge deficit. Finally, a “Proposal” step is performed, in which the “knowledge processes”—creation/acquisition, transference/distribution, and putting into practice/updating—are appraised to arrive at a coherent recommendation. The knowledge updating process (increasing the knowledge held and removing obsolete knowledge) is in itself a novel contribution. DIPKIP may be applied as a decision support system, which, under the supervision of a KM expert, can provide useful and practical proposals to senior management for the improvement of KM, leading to flexibility, cost savings, and greater competitiveness. The research also analyses the future for powerful neural projection models in the emerging field of KM by reviewing a variety of robust unsupervised projection architectures, all of which are used to visualize the intrinsic structure of high-dimensional data sets. The main projection architecture in this research, known as Cooperative Maximum-Likelihood Hebbian Learning (CMLHL), manages to capture a degree of KM topological ordering based on the application of cooperative lateral connections. The results of two real-life case studies in very different industrial sectors corroborated the relevance and viability of the DIPKIP system and the concepts upon which it is founded

A hybrid proposal for cross-sectoral analysis of knowledge management

At present time, although many theoretical formulations have been successfully proposed, there is a lack of ICT-based tools to support practical deployment of knowledge management (KM) in real settings. To bridge this gap, a hybrid artificial intelligence system is proposed in present study, aimed at gaining deeper knowledge about KM practices in four different economic sectors. By means of soft computing, companies are diagnosed according to their status regarding KM and subsequent explanations about crucial KM practices and perspectives are generated. Interesting conclusions are then derived from these explanations, allowing KM managers to optimise their decisions and obtain better results. Experimental results of real-life data from Spanish companies associated with different economic sectors validate the proposed combination of techniques

Analyzing Key Factors of Human Resources Management

This study presents the application of an unsupervised neural projection model for the analysis of Human Resources (HR) from a Knowledge Management (KM) standpoint. This work examines the critical role that the acquisition and retention of specialized employees play in Hi-tech companies, particularly following the configuration approach of Strategic HR Management. From the projections obtained through the connectionist models, experts in the field may extract conclusions related to some key factors of the HR Management. One of the main goals is to deploy improvement and efficiency actions in the implantation and execution of the HR practices in firms. The proposal is validated by means of an empirical study on a real case study related to the Spanish Hi-tech sector

A Hybrid Solution for Advice in the Knowledge Management Field

This paper presents a hybrid artificial intelligent solution that helps to automatically generate proposals, aimed at improving the internal states of organization units from a Knowledge Management (KM) point of view. This solution is based on the combination of the Case-Based Reasoning (CBR) and connectionist paradigms. The required outcome consists of customized solutions for different areas of expertise related to the organization units, once a lack of knowledge in any of those has been identified. On the other hand, the system is fed with KM data collected at the organization and unit contexts. This solution has been integrated in a KM system that additionally profiles the KM status of the whole organization

Constructing a Global and Integral Model of Business Management Using a CBR System

Knowledge has become the most strategic resource in the new business environment. A case-based reasoning system, which incorporates a novel clustering and retrieval method, has been developed for identifying critical situations in business processes. The proposed method is based on a Cooperative Maximum Likelihood Hebbian Learning model, which can be used to categorize the necessities for the Acquisition, Transfer and Updating of Knowledge of the different departments of a firm. This technique is used as a tool to develop a part of a Global and Integral Model of business Management, which brings about a global improvement in the firm, adding value, flexibility and competitiveness. From this perspective, the model tries to generalise the hypothesis of organizational survival and competitiveness, so that the organisation that is able to identify, strengthen, and use key knowledge will reach a pole position

Hybrid Visualization for Deep Insight into Knowledge Retention in Firms

Neural projection models are applied in this study to the analysis of Human Resources (HR) from a Knowledge Management (KM) standpoint. More precisely, data projections are combined with the glyph metaphor to analyse KM data and to gain deeper insight into patterns of knowledge retention. Following a preliminary study, the retention of specialized employees in hi-tech companies is investigated, by applying the configurational approach of Strategic HR Management. The combination of these two aforementioned techniques generates meaningful conclusions and the proposal is validated by means of an empirical study on a real case study related to the Spanish hi-tech sector

La gestión del capital intelectual en la empresa: hacer de cada participante lo mejor que pueda ser.

El objetivo de este trabajo es, primero, analizar una de las más significativas formas de conocimiento en la empresa, nos referimos al Capital Intelectual (CI), entendiendo por tal, la adecuada combinación entre capacidad y compr

A Beta-Cooperative CBR System for Constructing a Business Management Model

Knowledge has become the most strategic resource in the new business environment. A case-based reasoning system has been developed for identifying critical situations in business processes. The CBR system can be used to categorize the necessities for the Acquisition, Transfer and Updating of Knowledge of the different departments of a firm. This technique is used as a tool to develop a part of a Global and Integral Model of Business Management, which brings about a global improvement in the firm, adding value, flexibility and competitiveness. From this perspective, the data mining model tries to generalize the hypothesis of organizational survival and competitiveness, so that the organization that is able to identify, strengthen, and use key knowledge will reach a pole position. This case-based reasoning system incorporates a novel artificial neural architecture called Beta-Cooperative Learning in order to categorize the necessities for the Acquisition, Transfer and Updating of Knowledge of the different departments of a firm. This architecture is used to retrieve the most similar cases to a given subject