Optimizing referral reward programs under impression management considerations

We examine referral reward programs (RRP) that are intended for a service firm to encourage its current customers (inductors) to entice their friends (inductees) to purchase the firm’s service. By considering the interplay among the firm, the inductor, and the inductee, we solve a “nested” Stackelberg game so as to determine the optimal RRP in equilibrium. We determine the conditions under which it is optimal for the firm to reward the inductor only, reward the inductee only, or reward both. Also, our results suggest that RRP dominates direct marketing when the firm’s current market penetration or the inductor’s referral effectiveness is sufficiently high. We then extend our model to incorporate certain key impression management factors: the inductor’s intrinsic reward of making a positive impression by being seen as helping a friend, the inductor’s concerns about creating a negative impression when making an incentivized referral, and the inductee’s impression of the inductor’s credibility when an incentive is involved. In the presence of these impression management factors, we show that the firm should reward the inductee more and the inductor less. Under certain conditions, it is optimal for the firm to reward neither the inductor nor the inductee so that the optimal RRP relies purely on unincentivized word of mouth.     

Integration and Utilization of Management Science Activities in Organizations<Superscript>†</Superscript>

A large-scale field study was undertaken to attempt to improve our understanding of the experiences of operational research and related management science (OR/MS) activities in U.S. industries. Multiple interviews were conducted in 66 major firms. An initial analysis of the data suggests several parameters which might be important to the success of management science activities and which researchers may wish to consider in future efforts at theory building. Examples of such factors are: background of the OR/MS leaders and analysts; succession of leaders; amount of OR/MS exposure in the firm; extent of computer usage in the firm; organizational location; project missions; top management relations; client relations; staff relations; personality conflicts. Selected cases are presented to illustrate the interaction of many of these factors in the reported experiences of several firms. Interface relations are examined with special attention given to data collection and implementation problems. The increasing usage of liaison roles is noted. Further studies are being conducted to compare these U.S. experiences with those of OR/MS activities in other countries.     

Optimal risk and liquidity management with costly refinancing opportunities

In this paper we study risk and liquidity management decisions within an insurance firm. Risk management corresponds to decisions regarding proportional reinsurance, whereas liquidity management has two components: distribution of dividends and costly equity issuance. Contingent on whether proportional or fixed costs of reinvestment are considered, singular stochastic control or stochastic impulse control techniques are used to seek strategies that maximize the firm value. We find that, in a proportional-costs setting, the optimal strategies are always mixed in terms of risk management and refinancing. In contrast, when fixed issuance costs are too high relative to the firm’s profitability, optimal management does not involve refinancing. We provide analytical specifications of the optimal strategies, as well as a qualitative analysis of the interaction between refinancing and risk management.     

Empirical Evaluation of Hybrid Defaultable Bond Pricing Models

A four‐factor model (the extended model of Schmid and Zagst) is presented for pricing credit risk related instruments such as defaultable bonds or credit derivatives. It is an advancement of an earlier three‐factor model. In addition to a firm‐specific credit risk factor, a new systematic risk factor in the form of GDP growth rate is included. This new model is set in the context of other hybrid defaultable bond pricing models and empirically compared to specific representatives. We find that a model based only on firm‐specific variables is unable to capture changes in credit spreads completely. However, it is shown that in this model, market variables such as GDP growth rates, non‐defaultable interest rates and firm‐specific variables together significantly influence credit spread levels and changes.     

Short-term work scheduling with job assignment flexibility for a multi-fleet transport system

In recent years, planning and management of operations of transportation companies has become increasingly complex as tighter financial constraints affect the ability to respond to changing demands for travel. This paper investigates how a firm can reduce its total labor cost, enhance the flexibility of its operations and improve worker productivity and utilization by determining the right mix of jobs in work schedules. Using a method we have developed to generate low cost work schedules for bus drivers of an inter-city transport system in India, we study the changes in worker productivity and utilization when a mix of primary and secondary jobs is built in work schedules. We also investigate other factors that can strongly influence worker productivity and utilization such as the amount of overtime in work schedules, the level of job assignment flexibility, and staff size. Test results and discussions of managerial implications are presented.     

Models for collective decision making in industry

When actions are taken by individual managers within a firm to enhance certain performance criteria they may be found to have an adverse effect on other criteria. Changes in pricing policies and promotional activity, modifications in product design, increased mechanisation of production processes or operations, etc., are just examples of actions which in general do not have equally desirable consequences on the most commonly pursued objectives within the company, such as that of reducing unit costs, or those of increasing turnover, market share, profit levels and return on capital employed.In this paper simple models are developed and analysed, each enabling the user to assess explicitly the effects on the unit cost; the revenue and the profit of combined changes imposed on the system by management decisions or by external factors. It is suggested that these models be used as a basis for discussion by the managers involved with a view to reaching a consensus as to the desirable decisions that should be taken.     

Linking industrial competitiveness and productivity at the firm level

The present paper analytically demonstrates that the competitiveness and productivity levels of a firm are related to one another. An industrial competitiveness model is presented first and then an analytical linkage between the competitiveness and productivity levels is established. Also discussed are the managerial implications of this analytical linkage for competitive strategy formulation.     

The construction of a comprehensive model for production strategy evaluation

To acquire reasonable profits and to be competitive in the globalize market, more and more firms today have exploited various kinds of production strategies, such as outsourcing, joint venture, or some kind of strategic alliance. Since every production strategy has its benefits and costs and may bring a firm different opportunities and risks, which kind of production strategy is the most suitable for a firm to carry out is a difficult and complicated decision with a high degree of uncertainty. Therefore, this research proposes a comprehensive production strategy evaluation model that can facilitate such a decision making for a firm. The factors that should be considered for devising a production strategy are listed for the benefits, opportunities, costs and risks (BOCR) merits first, and fuzzy Delphi method is applied next to select the most important factors under each merit. A network with BOCR merits is constructed to incorporate the selected factors, and fuzzy analytic network process is then applied to consider the interdependence and feedback inside the network. The proposed model can effectively aid decision making on which kind of production strategy should be adopted by a firm. A case of a printed circuit board manufacturer is presented for the illustration of the proposed model.     

Comparative performance evaluation under uncertainty

In this article CCPR, a multidimensional framework for comparative performance evaluation is proposed, which is elaborated and illustrated through a real-life case. A particular feature of the approach is that it takes account of and corrects for the influence of risks, which are beyond the control of the decision maker. Here risk is seen as a multidimensional measure; it is expressed by means of sensitivities to unexpected changes of a multitude of risk factors. Furthermore, the approach corrects for differences in characteristics between the firms that are being compared. Some characteristics are fixed and thus uncontrollable for the decision maker, whereas others may be changed and controlled by the decision maker. The approach also answers whether the changes in firm characteristics made by management have been appropriate or not.     

Chapter 9 Moving frontier analysis: An application of Data Envelopment Analysis for competitive analysis of a high-technology manufacturing plant

Competitive analysis of plants is fundamental to enhancing the competitiveness of manufacturing firms. Moving Frontier Analysis (MFA), proposed in this paper, is a new application of Data Envelopment Analysis (DEA) to conduct competitive analysis of a high-technology manufacturing plant. The development and application of MFA was done in collaboration with the management of a US merchant semiconductor manufacturing firm. A wafer fabrication plant of the firm served as the research site for this study. MFA provides a means to determine (i) the gap between a plant's best performance and the best of competition, and (ii) whether or not it will be possible to close this performance gap, and if so, the time it will take to do so. From an implementation standpoint, competitive analysis using MFA can be conducted even if (i) accurate and detailed data on comparable plants of competitor companies are not available, and (ii) the operations of competitors' plants are dynamically changing. An application of MFA for conducting competitive analysis of the wafer fabrication plant is presented. This application illustrates how MFA makes it possible to estimate (i) the unit cost of production for the best of competitors' plants using aggregate operational information on industry best practices, and (ii) the time it will take for the plant's unit cost of production to catch up with the best of competition using the technologies in operation.     

Model testing for multivariate censored data

Summary. In the fields like Astronomy and Ecology, the need for proper statistical analysis of data that are censored is being increasingly recognized. Such data occur when, due to noise or other factors, instruments fail to detect low luminosities of celestial objects, or low concentrations of certain pollutants. For multivariate censored data sets there are very few distribution free methods available and researchers in the various fields often impose an assumption on the joint distribution, such as multivariate normality, and carry out parametric inferences. Under censoring, however, such parametric inferences are asymptotically wrong if the imposed assumption is incorrect. In this paper we propose a class of goodness-of-fit procedures for testing assumptions about the multivariate distribution under random censoring. The test procedures generalize Pearson's goodness-of-fit test in the sense that they are based on the concept of observed-minus-expected frequencies. The theory of the test statistic, however, differs from that for the classical Pearson test due to the accommodation of censored data. Received: 24 May 1994 / In revised form: 3 March 1996     

Cure events in default prediction

This paper evaluates the resurrection event regarding defaulted firms and incorporates observable cure events in the default prediction of SME. Due to the additional cure-related observable data, a completely new information set is applied to predict individual default and cure events. This is a new approach in credit risk that, to our knowledge, has not been followed yet. Different firm-specific and macroeconomic default and cure-event-influencing risk drivers are identified. The significant variables allow a firm-specific default risk evaluation combined with an individual risk reducing cure probability. The identification and incorporation of cure-relevant factors in the default risk framework enable lenders to support the complete resurrection of a firm in the case of its default and hence reduce the default risk itself. The estimations are developed with a database that contains 5930 mostly small and medium-sized German firms and a total of more than 23000 financial statements over a time horizon from January 2002 to December 2007. Due to the significant influence on the default risk probability as well as the bank’s possible profit prospects concerning a cured firm, it seems essential for risk management to incorporate the additional cure information into credit risk evaluation.     

Designing Firm Integrating Processes from the Knowledge-Based View—Graduate Student Best Paper Award, CASOS 2002 Conference

The knowledge-based view provides a powerful perspective for analyzing and understanding the firm. However, some of its assumptions have proven difficult to operationalize, making hypothesis testing difficult. One strategically important assumption is that firm competitive advantage depends on effective integrating processes. This paper develops a more precise specification of effective integrating processes. Then, two integrating processes are modeled using an agent-based simulation approach to test a set of factors believed to influence knowledge transfer and application. Results support several hypotheses generated by the knowledge-based view including: (1) greater levels of tacit knowledge transfer enhance the performance benefits gained from integrating processes, and (2) integrating processes with greater task uncertainty benefit from more formal information communication. The results also suggest that the design and management of firm integrating processes is a complex undertaking that requires a clear understanding of (1) the primary performance measure(s) used to evaluate the process, (2) the nature of the process tasks (e.g., routine vs. idiosyncratic), and (3) the knowledge required to perform those tasks (e.g., explicit vs. tacit).     

Measuring dynamic efficiency: Theories and an integrated methodology

Almost all dynamic production systems are subject to lagged productive effects, which are an often-ignored latent source of interference in the efficiency measuring process. Existing data envelopment analysis (DEA) approaches rely on a static production environment. They can easily lead to biased evaluation results due to the erroneous assumption. To tackle this issue, this paper develops a dynamic DEA model that allows intertemporal effects in efficiency measuring. Specifically, the dynamic DEA model incorporates dynamic factors via a linear parametric formulation. Our model can be applied in place of static DEA models to a wide range of applications, such as analyzing longitudinal firm performance and productivity changes. As for the empirical efficiencies, we demonstrate how the lag parameters in the dynamic model can be estimated by the panel vector autoregressive model (PVAR). We use our methodology to evaluate advertising efficiencies of several major automobile and pharmaceutical firms in North America. The result shows that using static DEA in dynamic production can lead to both rank reversals and changes in efficiency scores.     

Portfolio selection under DEA-based relative financial strength indicators: case of US industries

Fundamental analysis is an approach for evaluating a firm for its investment-worthiness whereby the firm's financial statements are subject to detailed investigation to predict future stock price performance. In this paper, we propose an approach to combine financial statement data using Data Envelopment Analysis to determine a relative financial strength (RFS) indicator. Such an indicator captures a firm's fundamental strength or competitiveness in comparison to all other firms in the industry/market segment. By analysing the correlation of the RFS indicator with the historical stock price returns within the industry, a well-informed assessment can be made about considering the firm in an equity portfolio. We test the proposed indicator with firms from the technology sector, using various US industries and report correlation analyses. Our preliminary computations using RFS indicator-based stock selection within mean–variance portfolio optimization demonstrate the validity of the proposed approach.     

Multivariate analysis for the assessment of factors affecting industrial competitiveness: The case of Greek food and beverage industries

Principal component analysis is integrated with canonical analysis to examine aspects of the competitiveness of two different sectors of the Greek manufacturing, i.e. the food and the beverage industries. Different measures of labour productivity, vertical integration, technological innovation and size of the firms which are considered as critical factors of industrial competitiveness are used in the application of the principal component analysis. Canonical analysis is then applied to correlate the variables of labour productivity with the other variables. In the case of the food and beverage integrated sector, the results of the principal component analysis pinpoint that there are two main principal components: (a) labour productivity and vertical integration, and (b) technological innovation and size. The first factor depicts the internal organizational, structural and production processes changes realized so that the competitiveness of the sector firms improves, whereas the second factor reflects the response of the sector to technological and growth trends. The two variables of labour productivity are affected by the degree of vertical integration and, at a lesser degree, by a common factor of the size of the firm and the level of investment for technological innovation. Copyright © 2000 John Wiley & Sons, Ltd.     

厂商低碳产品管理决策行为分析

在发展低碳经济的时代背景下,企业将面临低碳产品的管制,为符合低碳产品标准,企业将增加低碳产品的投资,对其生产工艺、技术或产品性能进行改造,势必增加企业经营成本,冲击产业发展.本文主要建立一个代表性厂商面临碳管制时低碳产品管理决策模型,期望通过厂商行为分析,掌握影响厂商低碳产品管理决策行为的关键因子,提供政府管理与厂商经营策略的参考意见.     

社会互动视角下的服务型企业最优运营策略研究

本文主要研究在社会互动的影响下,服务型企业如何制定以价格、服务速率为代表的运营管理策略,从而实现利润最大化。首先,基于M/M/1排队模型对顾客购买决策过程进行建模分析,考虑单个服务型企业在实现利润最大化的目标条件下,社会互动的作用如何影响定价和服务速率决策;其次,研究在竞争市场环境中,社会互动如何影响市场均衡状态下各企业的运营决策和市场份额。结果表明,无论在垄断还是竞争的市场环境中,社会互动对服务型企业的最优运营决策均有不同程度与方向的影响。     

Application of consistent fuzzy preference relations in predicting the success of knowledge management implementation

The implementation of knowledge management (KM) involves innovation and reformation for organizations. KM implementation requires not only a substantial investment, but also changes the organization culture and structure. Before embarking on KM, thorough planning is crucial to ensure the implementation achieves the intended objectives of accruing profit and enhancing competitiveness for organisations. Therefore, this study proposes an analytic hierarchical prediction model based on the reciprocal additive consistent fuzzy preference relations to help the organizations become aware of the essential factors affecting the KM implementation, forecasting the chance of successful KM initiative, as well as identifying the actions necessary before implementing KM. Pairwise comparisons are used to determine the priority weights of influential factors and the ratings of two possible outcomes (success and failure) amongst decision makers. The subjectivity and vagueness in the prediction procedures are dealt with using linguistic terms quantified in an interval scale [0, 1]. By multiplying the weights of influential factors and the ratings of possible outcomes, predicted success/failure values are obtained to enable organizations to decide whether to initiate knowledge management, inhibit adoption or take remedial actions to increase the possibility of successful KM project. This proposed approach is demonstrated with a real case study assessed by eleven evaluators solicited from a Liquid Crystal Display (LCD) manufacturing corporation located in Taiwan.     

基于半参数分位数回归的居民个人收入研究

教育、性别、年龄、城乡、地区经济都是影响居民个人收入的重要因素,这些因素影响收入的作用方式是不同的,有的以参数形式存在,有的则是非参数形式存在.同时,这些因素在收入的不同分位点影响收入的程度也是不同的,通过构造半参数分位数回归模型,对影响收入诸因素的作用方式与程度进行了研究.