Beating the odds in the innovation arena: The role of market and technology signals classification and noise

Innovation is a risky business. Notwithstanding the attention in research on the determinants of innovation success, the estimates of the extremely high failure rates (ranging from 40% to 90%) have not come down. As a result, new approaches have to be found to address the issues that prevent organizations from reaching innovation success over a longer period of time. In this study, Signal Detection Theory (SDT) is introduced to show that it may be less important to improve innovation practices in companies, than it is to change the nature of the projects that enter the corporate innovation funnel. In our empirical study of 44 innovation projects in the pharmaceutical and electronics industry, we show that pursuing more noisy signals (uncertain technological and market opportunities) is currently beneficial for innovation success. In general, companies allocate too few resources to noisy innovation opportunities. Interestingly, we find a positive, and not an inverted U-shaped, relationship between noise/signal ratios and innovation success. This indicates that there are almost no companies that take too much risk in the current competitive environment and that many can gain from increasing their noise tolerance. The implications from this study are quite counter-intuitive in a sense that ‘risk’ is not the cause of the innovation problems, but may be a solution.     

Network agility as a trigger for enhancing firm performance: A case study of a high-tech firm implementing the mixed channel strategy

Original equipment manufacturers (OEMs) can reduce labor costs, free up capital investment and enhance firm's agility by full outsourcing of product manufacturing. OEMs can then focus on the most valuable processes, e.g. R&D and marketing. However, contract manufacturers (CMs) may decide to foster their own brand and forge their own relationships with retailers or distribution channels. The conflicts of interest between OEMs and CMs can be reduced by implementing a mixed channel strategy. Label licensing, the most essential component in the implementation, enhances the marketing capability of a CM and allows it to move up the value chain. In this research, a case study involving a supply chain network in the optical storage media industry is used to develop a conceptual model to explain how a mixed channel strategy and superior network agility enhance firm performance. This study argues that information system integration influences firm performance through network agility, and that the mixed channel strategy boosts financial performance. Moreover, the result proposes that network agility is associated with a moderating effect on the relationship between the mixed channel strategy and financial performance.     

The impact of marketing capability, operations capability and diversification strategy on performance: A resource-based view

Using resource-based view (RBV) of the firm as a theoretical backdrop; we aim to find out the relative impact of a firm's functional capabilities (namely, marketing and operations) and diversification strategies (product/service and international diversification) on financial performance. We hypothesize that this linkage depends on the firm's relative efficiency to integrate its resource-capabilities-performance triad. Using archival data of 102 UK based logistics companies, we find marketing capability is the key determinant for superior financial performance. This study highlights that a market-driven firm is likely to have better business performance than a firm focusing solely on operational capabilities. Also, firms are better off when they focus on a narrow portfolio of products/services for the clients and concentrate on a diverse geographical market. Our findings provide a new perspective to model a firm's functional capabilities and diversification strategy on its financial performance and offer a benchmarking tool to improve resource allocation decisions.     

A CRM performance measurement framework: Its development process and application

We suggest a performance measurement framework called a customer relationship management (CRM) scorecard to diagnose and assess a firm's CRM practice. The CRM scorecard was developed through a rigorous and stepwise development process collaborated with a number of firms in a variety of industries. During the development process, we conducted an extensive literature review to build a theoretical causal map, in-depth interviews with practitioners to extract a hierarchical map from industrial perspectives, feasibility tests to check whether or not Key Performance Indicators (KPI) could be measured, and Analytic Hierarchy Process (AHP) analysis to prioritize the evaluation factors on the CRM scorecard. The CRM scorecard contains antecedent/subsequent and objective/perceptual evaluation factors in four different perspectives to comprehensively measure corporate CRM capability and readiness. To illustrate the applicability of the proposed CRM scorecard, we apply the framework to a retail bank in Korea well-known for its exemplary CRM strategy.     

The Determinants of Entrepreneurial Intention of Young Researchers: Combining the Theory of Planned Behavior with the Triple Helix Model

Although the theme of academic spin‐off has received increasing consideration in entrepreneurship literature, little attention has been devoted to identifying the factors that drive young researchers to set up ventures based on the results of their research. To identify the determinants of academic entrepreneurial intention (AEI), we tested a model on a sample of Italian researchers using structural equation modeling and integrating the Triple Helix Model with the theory of planned behavior (TPB). The findings highlight that all psychological variables of TPB are relevant in predicting AEI, whereas only some contextual and exogenous variables (namely, government and industrial/financial support) directly influence AEI.     

Estimating causal effects of credit decisions

In principle, making credit decisions under uncertainty can be approached by estimating the potential future outcomes that will result from the various decision alternatives. In practice, estimation difficulties may arise as a result of selection bias and limited historic testing. We review some theoretical results and practical estimation tools from observation study design and causal modeling, and evaluate their relevance to credit decision problems. Building on these results and tools, we propose a novel approach for estimating potential outcomes for credit decisions with multiple alternatives based on matching on multiple propensity scores. We demonstrate the approach and discuss results for risk-based pricing and credit line increase problems. Among the strengths of our approach are its transparency about data support for the estimates and its ability to incorporate prior knowledge in the extrapolative inference of treatment-response curves.     

Performance monitoring of credit portfolios using survival analysis

We are interested in detecting changes in the performance of a credit portfolio quickly and robustly. The portfolio is dynamic: customers can either default or pay the full amount, and new customers can be taken on. Robust detection means that changing the number of new customers taken on should not lead to either a false or delayed signal. We investigate the performances of monitoring schemes via a simulation study that uses several scenarios. We consider monitoring based on default rates estimated through a gliding window, cumulative sum (CUSUM) charts based on default rates, CUSUM charts based on defaults within a given follow-up time after arrival, and a survival analysis CUSUM chart. We conclude that using a survival analysis approach is preferable to using the other approaches.     

Instance sampling in credit scoring: An empirical study of sample size and balancing

To date, best practice in sampling credit applicants has been established based largely on expert opinion, which generally recommends that small samples of 1500 instances each of both goods and bads are sufficient, and that the heavily biased datasets observed should be balanced by undersampling the majority class. Consequently, the topics of sample sizes and sample balance have not been subject to either formal study in credit scoring, or empirical evaluations across different data conditions and algorithms of varying efficiency. This paper describes an empirical study of instance sampling in predicting consumer repayment behaviour, evaluating the relative accuracies of logistic regression, discriminant analysis, decision trees and neural networks on two datasets across 20 samples of increasing size and 29 rebalanced sample distributions created by gradually under- and over-sampling the goods and bads respectively. The paper makes a practical contribution to model building on credit scoring datasets, and provides evidence that using samples larger than those recommended in credit scoring practice provides a significant increase in accuracy across algorithms.