Benchmarking airports from a managerial perspective

Benchmarking airports is currently popular both in the academic literature and in practice but has proved rather problematic due to the heterogeneity inherent in any reasonably sized dataset. Most studies either treat the airport production technology as a black box, or separate the terminal and airside activities, assessing them individually. In this article we analyze airports as a single unit due to the direct complementarities, thus avoiding the artificial separation of inputs. Using data envelopment analysis (DEA), we open the black box in which a network describes the production process, thus demonstrating the sequential effects that separate final from intermediate outputs, including those under partial managerial control and those that are known to be non-discretionary. To further improve the benchmarking process, we identify appropriate peers for a case study of 43 European airports over 10 years, through a restricted reference mechanism according to pre-defined characteristics. Compared to basic DEA models, the results of the proposed structure provide more meaningful benchmarks with comparable peer units and target values that are potentially achievable in the medium term. By identifying each unit's individual reference set, unique outliers influence the performance measurement less severely than occurs under basic DEA. In addition, the formulations produce an implementation path that moves the airport towards the Pareto frontier gradually, taking into account the regulatory and business environment in which the unit is located.     

A generalisation of the Farrell cost efficiency measure applicable to non-fully competitive settings

This paper enhances cost efficiency measurement methods to account for situations where the input prices can depend on negotiation or tend to qualitatively differentiate the resources available at each decision making unit. In these circumstances, there are some shortcomings in the cost efficiency measure described in the data envelopment analysis (DEA) literature. This paper proposes new models and measures for cost efficiency evaluation that overcome the limitations of the existing DEA models. The applicability of the measures developed is illustrated in the context of the analysis of bank branch performance.     

只有输出的广义样本区间DEA模型

在应用多个绩效指标综合评价决策单元有效性时，决策者常常把这些决策单元与另外预先指定的标准（样本单元）进行比较。由于客观事物的复杂性和不确定性导致样本单元和决策单元的指标信息有时必须用区间数的形式给出。针对区间数指标信息的综合评价问题，本文通过分解的方法讨论样本单元和决策单元指标信息为区间数时用广义DEA模型评价决策单元有效性的方法，并相应地构建了只有输出的广义区间DEA模型。同时，对模型的含义、求解以及性质等进行了分析。之后，探讨了该方法在决策单元有效性分类和排序中的应用。最后，通过实例表明该方法的可行性和有效性。     

Generalized symmetric weight assignment technique: Incorporating managerial preferences in data envelopment analysis using a penalty function

In this paper, we generalize the Symmetric Weight Assignment Technique to incorporate all managerial preferences in Data Envelopment Analysis (DEA). This is a method that promotes managerial preferences, while not changing the feasibility region of the nominal DEA model, unlike standard techniques such as cone ratio and assurance region that potentially yield infeasible problems. We discuss how this generalization is motivated by a real word problem where the decision maker's preference is not precisely known. We also discuss how we are using our generalization in solving a workforce allocation problem for the United States Navy.     

The Influences of User Characteristics on Performance in a Logistics DSS Application

Decision Support Systems (DSS) are widely used in logistics decision applications, and a large number and variety of systems are commercially available. We investigate the contributions of user characteristics including experiences, data preferences, intuition, and effort to decision performance in a logistics DSS context. The study includes a laboratory experiment in which decision makers with varied experiences used a DSS to make facility network design decisions for problems of varying complexity. Two variants of the DSS are utilized in order to examine the interactions of a DSS decision aid with user characteristics. We find that intuition and effort are associated with decision-making performance. High analytic ability is not related to intuition, however. Education and previous experience are associated with performance. Yet these characteristics are also unrelated to intuition. Decision makers who highly value disaggregated data provided by the DSS tend to perform poorly. Also, the results suggest that the effects of users' experiences and preferences on performance are influenced by an analytical decision aid.     

Data and Model Management in a Generalized MCDM-DSS*

Drawing upon the choice models developed in the multiple criteria decision making (MCDM) area, this paper proposes an architecture for designing an intelligent decision support system (DSS) that is intended to aid in making choices among multiple alternatives along multiple dimensions. It argues that effective support can be provided to the decision maker when the knowledge-based DSS is capable of dynamically selecting choice models appropriate to the domain and context of a particular problem being specified by the decision maker, and of properly applying them to the problem solution. Development of a prototype intended to partially represent application of the architecture is described. The paper concludes with suggestions for research extensions.     

The Significance of Information Systems Research on Emerging Technologies: Seven Information Technologies that Promise to Improve Managerial Effectiveness*

Emerging information technologies are those excitingly novel advances that decision makers are just beginning to notice. These emerging products or processes may have been invented some time ago, but are only now beginning to reveal practical applications. This paper examines this collection of seven studies exploring emerging technologies, classifying them so the context of their results can be related to other emerging technologies. Once the research is classified and the contribution and managerial significance of the research is identified, it becomes apparent that certain barriers exist, holding back the technology from wider use and appreciation. Common obstacles include resistance to, or difficulty with, using the technology; uncertainty concerning the value of the technology; and the complexities involved with implementation. This paper concludes with recommendations for future research aimed at breaking down the barriers in order to move from the technological emergence phase to the technological sublime phase in which decision makers and other end users understand the technology, realize and appreciate its value, and put the technology to its best use.     

Data envelopment analysis: Prior to choosing a model

In this paper, we address several issues related to the use of data envelopment analysis (DEA). These issues include model orientation, input and output selection/definition, the use of mixed and raw data, and the number of inputs and outputs to use versus the number of decision making units (DMUs). We believe that within the DEA community, researchers, practitioners, and reviewers may have concerns and, in many cases, incorrect views about these issues. Some of the concerns stem from what is perceived as being the purpose of the DEA exercise. While the DEA frontier can rightly be viewed as a production frontier, it must be remembered that ultimately DEA is a method for performance evaluation and benchmarking against best-practice. DEA can be viewed as a tool for multiple-criteria evaluation problems where DMUs are alternatives and each DMU is represented by its performance in multiple criteria which are coined/classified as DEA inputs and outputs. The purpose of this paper is to offer some clarification and direction on these matters.     

Cross-benchmarking for performance evaluation: Looking across best practices of different peer groups using DEA

In benchmarking, organizations look outward to examine others’ performance in their industry or sector. Often, they can learn from the best practices of some of them and improve. In order to develop this idea within the framework of Data Envelopment Analysis (DEA), this paper extends the common benchmarking framework proposed in Ruiz and Sirvent (2016) to an approach based on the benchmarking of decision making units (DMUs) against several reference sets. We refer to this approach as cross-benchmarking. First, we design a procedure aimed at making a selection of reference sets (as defined in DEA), which establish the common framework for the benchmarking. Next, benchmarking models are formulated which allow us to set the closest targets relative to the reference sets selected. The availability of a wider spectrum of targets may offer managers the possibility of choosing among alternative ways for improvements, taking into account what can be learned from the best practices of different peer groups. Thus, cross-benchmarking is a flexible tool that can support a process of future planning while considering different managerial implications.     

An Evaluation of the Role and Performance of a Decision Support System in Business Education*

With the rapid growth in end-user computing, there has been an increasing demand on business schools to train managers in the use of computers for effective decision support. While computer-aided instruction (CAI) has been in vogue for over a decade, there are very few studies that evaluate the impact of computers on decision-making skills in a business education context. The study makes use of a controlled experiment to evaluate the impact of computers on the accuracy and quality of decision making in a business decision support context. It also addresses the relationships between decision performance and various student characteristics such as aptitude, attitude, domain experience, domain expertise, gender, and system experience. The study revealed that computer support positively influenced both dimensions of decision performance.     

Examining the Antecedents and Consequences of CIO Strategic Decision‐Making Authority: An Empirical Study*

Despite the strategic importance of information technology (IT) to contemporary firms, chief information officers (CIO) often still have varying degrees of strategic decision‐making authority. In this study, we apply the theory of managerial discretion to define CIO strategic decision‐making authority and argue that the CIO's level of strategic decision‐making authority directly influences IT's contribution to organization performance. We also draw on the power and politics perspective in the strategic decision‐making literature to identify the direct antecedents to the CIO's strategic decision‐making authority. A theoretical model is presented and empirically tested using survey data collected from a cross‐industry sample of 174 matched pairs of CIOs and top business executives through structural equation modeling. The results suggest that organizational climate, organizational support for IT, the CIO's structural power, the CIO's level of strategic effectiveness, and a strong partnership between the CIO and top management team directly influence the CIO's level of strategic decision‐making authority within the organization. The results also suggest that the CIO's strategic decision‐making authority in the organization directly influences the contribution of IT to firm performance and that effective CIOs have a greater influence on IT's contribution when provided with strategic decision‐making authority.     

Culminating events and time working together in top management teams: Insights from private equity

In some managerial contexts, performance is measured by the results of a rare event that is itself the culmination of a long and ongoing process of daily decision making. In the private equity (PE) industry, for example, a firm makes a limited number of investments throughout its life. However, selecting these few investments requires the firm to engage in a large number of decision-making activities to evaluate the much larger array of potential investments. This raises the interesting possibility that the performance of each culminating event is not a function of the number of previously completed tasks, as it has been conceived previously, but is in fact a function of the accumulation of daily decision making that may or may not culminate in actual buyouts. To understand performance drivers in this and similar contexts, we examine and test the performance effect of the time that members of a PE top management team (TMT) spend working together to evaluate potential investments, and untangle this effect from the number of investments the TMT has bought and sold previously. Our data reveal a U-shaped relationship between time working together (TWT) and buyout performance, and a moderation effect of TMT heterogeneity and TMT support staff size. These findings extend our knowledge of executive decision making in PE firms, and in all managerial contexts where rare decision events are the culmination of an extended process of daily decision making.     

The Effects of Group Attitudes Toward Alternative GDSS Designs on the Decision-making Performance of Computer-Supported Groups*

Empirical investigations of the effects of group decision support systems (GDSS) accord relatively little attention to the role of attitudes developed by groups toward the GDSS. This study draws upon the theory of social influence to examine the role played by group attitudes in mediating the impact of GDSS designs on group decision-making performance. We found that, in addition to capabilities of GDSS designs, group attitudes toward GDSS were influential in affecting group decision-making performance. The managerial implications of the study are: (1) effective GDSS designs for supporting groups in equivocal decision-making contexts should include structures for communication and consensus support and (2) attempts should be made to enhance user attitudes toward the GDSS through design features of the GDSS, training, and facilitation of positive social influences within the group. The study also provides encouraging evidence about the value of social theories, such as the social influence model and the adaptive structuration theory, in investigating GDSS effects. Further, this study illustrates the value of the partial least squares (PLS) analysis method for testing holistic structural models of GDSS effects.     

The Present Value Issue in a Sequential Selection Problem*

This note shows that, due to the misapplication of the present value concept, the Chun and Platt (CP) formulation of a sequential selection problem, which intends to improve on the Kwan and Yuan (KY) analysis, is flawed and the decision criterion thus obtained is invalid. In the context of capital budgeting, we make necessary corrections to the CP analysis and establish a decision criterion which is more refined than the KY case. The refined decision criterion enables us to evaluate the KY analysis objectively and to refute CP's argument about its suboptimality. We also address the managerial implications of the refined decision criterion.     

Business groups' outward FDI: A managerial resources perspective

Outward FDI strategies are driven by firms' resource endowments, which in turn are conditioned by their home environment. In emerging economies, thus, the pattern of outward FDI is shaped by local firms' idiosyncratic contexts and the resources that these firms developed to fit the contexts. This includes business groups, a dominant organizational form in many emerging economies, competing with context-bound resources. When they wish to transcend their home context, they need internationally valuable resources, especially managerial resources, which may be quite different than the resources that enable domestic growth. This paper thus explores what resources drive this international growth in the case of Taiwanese business groups. Starting from Penrosian Theory, we focus on managerial resources that are shared across the member firms of a group, and thus shape the profile of the group. We find that international work experience favors internationalization while international education does not. Moreover, domestic institutional resources distract from internationalization, presumably because they are not transferable into other institutional contexts, and thus favor other types of growth.     

A fair evaluation of certain stage in a two-stage structure: revisiting the typical two-stage DEA approaches

The efficiency of decision processes with a two-stage structure has been studied by some modified versions of Data Envelopment Analysis (DEA) methodology, such as the relational or centralized model, and the non-cooperative model. After revisiting the rationale of the centralized model provided by the literature, we find that some unfairness exist in its efficiency evaluation of certain stage. This unfairness leads to the usual underestimation of the overall efficiency by the centralized model. Furthermore, because the independent DEA model for one stage ignores its relation and coordination with the other stage and the two-stage system, externalities between these members may arise and lead to the seeming contradiction in efficiencies derived by independent DEA models for the stages and the black box system. Therefore, we argue that the efficiency of certain stage in the context of a two-stage structure should be reevaluated instead of simply using the independent DEA model. A sequence of leader-follower procedures, with data adjustment on intermediates, is proposed to eliminate the externalities and to ensure a fair evaluation. We find that, after this data adjustment, the reevaluation of the second stage for a given Decision Making Unit (DMU) yields the same result as obtained by the standard DEA model regarding the two-stage as a black box. Moreover, some explicit relations are established between the black box model, the centralized model, and the non-cooperative model. Two typical examples taken from the literature illustrate our main results. Our findings also imply that more emphasis should be placed on the game theoretic DEA approach to model the efficiency evaluation of two-stage processes.     

Interactive LP models in management—An analogue approach

The history is traced over 10 years of a project directed towards extending the use of Linear Programme methods by managers and planners themselves. Both computational aspects as they affect the end user, and possible alternatives to digital computation, are discussed in the light of practical experience and of recently proposed criteria for mathematical modelling in a managerial environment. The operating principles of an LP-based electronic analogue system are described, along with some of its observed implications in terms of human behaviour. It is concluded that LP and related techniques have hardly begun to exploit their potential, both as a control medium and as a language of managerial communication; that the concept of direct optimisation is largely irrelevant and has formed a barrier against implementation; and that the field of application for LP models is far wider than can be adduced by rigorous analysis, or by working to the practical bounds of digital computation.     

The Role of Operational Interdependence and Supervisory Experience on Management Assessments of Resource Planning Systems

Greater interdependence among workers and activities not only increases the need for internal communication, but it also imposes complications and barriers to effective information exchange. Intraorganizational communication capabilities of certain information systems can help overcome these barriers. However, the extent to which certain systems are promoted as communication tools depends largely on management's interpretation of their usefulness, which in turn may be largely dependent on operational context and managerial experience. We use a controlled experimental approach to study how these issues interact to impact managerial assessments of resource planning systems. Results show that managers value the communication capabilities of resource planning systems more so in highly task‐interdependent contexts and that these assessments are still more positive among managers with greater supervisory experience. As a result, these findings pose direct implications regarding the management support of technology use.     

Benchmarking of maintenance and outage repair in an electricity distribution company using the value-based DEA method

Benchmarking of electricity distribution utilities has been widely used as a means to contribute for the adoption or reinforcement of enhanced competitiveness and innovation practices to optimize costs, increase customer satisfaction, improve corporate image and maximize profits. The purpose of this paper is to present a benchmarking study for the maintenance and outage repair activity carried out by a Portuguese electricity distribution company, EDP Distribuição (EDP-D), using the value-based DEA method, which builds on links between data envelopment analysis (DEA) and multiple criteria decision analysis (MCDA). This study illustrates the impact of the incorporation of managerial preferences in the classification and ranking of 40 network areas served by EDP-D, confronting the results with a previous study based on a BCC DEA model. In order to deal with the underlying uncertainty, the value-based DEA method for performance evaluation is adapted to include the concept of super-efficiency. Besides identifying best practices, sources of inefficiency, gaps relatively to best practices and opportunities for improvement, this analysis supports the introduction of corrective measures and informs decisions about future goals.     

The Effectiveness of Decisional Guidance: An Empirical Evaluation

Decisional guidance is defined as how a decision support system (DSS) influences its users as they structure and execute the decision‐making process. It is assumed that decisional guidance has profound effects on decision making, but these effects are understudied and empirically unproven. This paper describes an empirical, laboratory‐experiment‐based evaluation of the effectiveness of deliberate decisional guidance and its four types. We developed and used a comprehensive model consisting of four evaluation criteria: decision quality, user satisfaction, user learning, and decision‐making efficiency. On these criteria, we compared decisional guidance versus no guidance, informative versus suggestive decisional guidance, and predefined versus dynamic decisional guidance. We found that deliberate decisional guidance was more effective on all four criteria; suggestive guidance was more effective in improving decision quality and user satisfaction, and informative guidance was more effective in user learning about the problem domain, whereas dynamic guidance was more effective than predefined guidance in improving decision quality and user learning; and both suggestive guidance and dynamic guidance reduced the decision time.