Suchergebnisse

Purpose
– Bankruptcy is a financial failure of a business or an organization. Different kinds of bankruptcy prediction techniques are proposed to predict it. But, they are restricted as techniques in predicting the bankruptcy and not addressing the associated activities like acquiring the suitable data and delivering the results to the user after processing it. This situation demands to look for a comprehensive solution for predicting bankruptcy with intelligence. The paper aims to discuss these issues.


Design/methodology/approach
– To model Business Intelligence (BI) solution for BP the concept of reference model is used. A Reference Model for Business Intelligence to Predict Bankruptcy (RMBIPB) is designed by applying unit operations as hierarchical structure with abstract components. The layers of RMBIPB are constructed from the hierarchical structure of the model and the components, which are part of the reference model. In this model, each layer is designed based on the functional requirements of the Business Intelligence System (BIS).


Findings
– This reference model exhibits the non functional software qualities intended for the appropriate unit operations. It has flexible design in which techniques are selected with minimal effort to conduct the bankruptcy prediction. The same reference model for another domain can be implemented with different kinds of techniques for bankruptcy prediction.


Research limitations/implications
– This model is designed using unit operations and the software qualities exhibited by RMBIPB are limited by unit operations. The data set which is applied in RMBIPB is limited to Indian banks.


Originality/value
– A comprehensive bankruptcy prediction model using BI with customized reporting.

AbstractThis research aims to investigate how knowledge sharing and innovation are linked to affect performance. A multi‐layered moderated‐mediation model is developed to explore the effect of interactions between four types of innovations (product, process, marketing, and organizational) on the relationship between knowledge conversion and performance. After checking the measurement properties of the survey instrument using the LISREL package of structural equation modeling (SEM), data collected from 312 employees from the southern part of India were analyzed using Hayes's PROCESS macros. The results indicate that (i) knowledge conversion is positively associated with (a) performance and (b) organizational innovation; (ii) organizational innovation is positively associated with performance; and (iii) organizational structure mediates the relationship between knowledge conversion and performance. The findings also indicate that process innovation (second moderator) positively moderates the relationship between knowledge conversion and product innovation (first moderator) in influencing organizational innovation. The study also found that marketing innovation moderates the relationship between organizational innovation and performance. Thus, the study underscores the importance of knowledge conversion and all four types of innovation in enhancing performance. The managerial implications are discussed.