This paper presents a set of basic components which constitutes the experimental setting intended for the evaluation of interactive information retrieval (IIR) systems, the aim of…
Abstract
This paper presents a set of basic components which constitutes the experimental setting intended for the evaluation of interactive information retrieval (IIR) systems, the aim of which is to facilitate evaluation of IIR systems in a way which is as close as possible to realistic IR processes. The experimental setting consists of three components: (1) the involvement of potential users as test persons; (2) the application of dynamic and individual information needs; and (3) the use of multidimensional and dynamic relevance judgements. Hidden under the information need component is the essential central sub‐component, the simulated work task situation, the tool that triggers the (simulated) dynamic information needs. This paper also reports on the empirical findings of the metaevaluation of the application of this sub‐component, the purpose of which is to discover whether the application of simulated work task situations to future evaluation of IIR systems can be recommended. Investigations are carried out to determine whether any search behavioural differences exist between test persons‘ treatment of their own real information needs versus simulated information needs. The hypothesis is that if no difference exists one can correctly substitute real information needs with simulated information needs through the application of simulated work task situations. The empirical results of the meta‐evaluation provide positive evidence for the application of simulated work task situations to the evaluation of IIR systems. The results also indicate that tailoring work task situations to the group of test persons is important in motivating them. Furthermore, the results of the evaluation show that different versions of semantic openness of the simulated situations make no difference to the test persons’ search treatment.
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Andreas Gschwentner, Manfred Kaltenbacher, Barbara Kaltenbacher and Klaus Roppert
Performing accurate numerical simulations of electrical drives, the precise knowledge of the local magnetic material properties is of utmost importance. Due to the various…
Abstract
Purpose
Performing accurate numerical simulations of electrical drives, the precise knowledge of the local magnetic material properties is of utmost importance. Due to the various manufacturing steps, e.g. heat treatment or cutting techniques, the magnetic material properties can strongly vary locally, and the assumption of homogenized global material parameters is no longer feasible. This paper aims to present the general methodology and two different solution strategies for determining the local magnetic material properties using reference and simulation data.
Design/methodology/approach
The general methodology combines methods based on measurement, numerical simulation and solving an inverse problem. Therefore, a sensor-actuator system is used to characterize electrical steel sheets locally. Based on the measurement data and results from the finite element simulation, the inverse problem is solved with two different solution strategies. The first one is a quasi Newton method (QNM) using Broyden's update formula to approximate the Jacobian and the second is an adjoint method. For comparison of both methods regarding convergence and efficiency, an artificial example with a linear material model is considered.
Findings
The QNM and the adjoint method show similar convergence behavior for two different cutting-edge effects. Furthermore, considering a priori information improved the convergence rate. However, no impact on the stability and the remaining error is observed.
Originality/value
The presented methodology enables a fast and simple determination of the local magnetic material properties of electrical steel sheets without the need for a large number of samples or special preparation procedures.
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Todd E. Clark and Michael W. McCracken
This article surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by vector autoregressions. Specific emphasis is placed…
Abstract
This article surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by vector autoregressions. Specific emphasis is placed on highlighting those parts of the existing literature that are applicable to direct multistep forecasts and those parts that are applicable to iterated multistep forecasts. This literature includes advancements in the evaluation of forecasts in population (based on true, unknown model coefficients) and the evaluation of forecasts in the finite sample (based on estimated model coefficients). The article then examines in Monte Carlo experiments the finite-sample properties of some tests of equal forecast accuracy, focusing on the comparison of VAR forecasts to AR forecasts. These experiments show the tests to behave as should be expected given the theory. For example, using critical values obtained by bootstrap methods, tests of equal accuracy in population have empirical size about equal to nominal size.
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Asli Ogunc and Randall C. Campbell
Advances in Econometrics is a series of research volumes first published in 1982 by JAI Press. The authors present an update to the history of the Advances in Econometrics series…
Abstract
Advances in Econometrics is a series of research volumes first published in 1982 by JAI Press. The authors present an update to the history of the Advances in Econometrics series. The initial history, published in 2012 for the 30th Anniversary Volume, describes key events in the history of the series and provides information about key authors and contributors to Advances in Econometrics. The authors update the original history and discuss significant changes that have occurred since 2012. These changes include the addition of five new Senior Co-Editors, seven new AIE Fellows, an expansion of the AIE conferences throughout the United States and abroad, and the increase in the number of citations for the series from 7,473 in 2012 to over 25,000 by 2022.
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Shankar Chakraborty and Soumava Boral
Subtractive manufacturing process is the controlled removal of unwanted material from the parent workpiece for having the desired shape and size of the product. Several types of…
Abstract
Purpose
Subtractive manufacturing process is the controlled removal of unwanted material from the parent workpiece for having the desired shape and size of the product. Several types of available machine tools are utilized to carry out this manufacturing operation. Selection of the most appropriate machine tool is thus one of the most crucial factors in deciding the success of a manufacturing organization. Ill-suited machine tool may often lead to reduced productivity, flexibility, precision and poor responsiveness. Choosing the best suited machine tool for a specific machining operation becomes more complex, as the process engineers have to consider a diverse range of available alternatives based on a set of conflicting criteria. The paper aims to discuss these issues.
Design/methodology/approach
Case-based reasoning (CBR), an amalgamated domain of artificial intelligence and human cognitive process, has already been proven to be an effective tool for ill-defined and unstructured problems. It imitates human reasoning process, using specific knowledge accumulated from the previously encountered situations to solve new problems. This paper elucidates development and application of a CBR system for machine tool selection while fulfilling varying user defined requirements. Here, based on some specified process characteristic values, past similar cases are retrieved and reused to solve a current machine tool selection problem.
Findings
A software prototype is also developed in Visual BASIC 6.0 and three real time examples are illustrated to validate the application potentiality of CBR system for the said purpose.
Originality/value
The developed CBR system for machine tool selection retrieves a set of similar cases and selects the best matched case nearest to the given query set. It can successfully provide a reasonable solution to a given machine tool selection problem where there is a paucity of expert knowledge. It can also guide the process engineers in setting various parametric combinations for achieving maximum machining performance from the selected machine tool, although fine-tuning of those settings may often be required.
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Jae Kyeong Kim, Hyun Sil Moon, Byong Ju An and Il Young Choi
Many off-line retailers have experienced a slump in sales and have the potential risk of overstock or understock. To overcome these problems, retailers have applied data mining…
Abstract
Purpose
Many off-line retailers have experienced a slump in sales and have the potential risk of overstock or understock. To overcome these problems, retailers have applied data mining techniques, such as association rule mining or sequential association rule mining, to increase sales and predict product demand. However, because these techniques cannot generate shopper-centric rules, many off-line shoppers are often inconvenienced after writing their shopping lists carefully and comprehensively. Therefore, the purpose of this paper is to propose a personalized recommendation methodology for off-line grocery shoppers.
Design/methodology/approach
This paper employs a Markov chain model to generate recommendations for the shopper’s next shopping basket. The proposed methodology is based on the knowledge of both purchased products and purchase sequences. This paper compares the proposed methodology with a traditional collaborative filtering (CF)-based system, a bestseller-based system and a Markov-chain-based system as benchmark systems.
Findings
The proposed methodology achieves improvements of 15.87, 14.06 and 37.74 percent with respect to the CF-, Markov chain-, and best-seller-based benchmark systems, respectively, meaning that not only the purchased products but also the purchase sequences are important elements in the personalization of grocery recommendations.
Originality/value
Most of the previous studies on this topic have proposed on-line recommendation methodologies. However, because off-line stores collect transaction data from point-of-sale devices, this research proposes a methodology based on purchased products and purchase patterns for off-line grocery recommendations. In practice, this study implies that both purchased products and purchase sequences are viable elements in off-line grocery recommendations.
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Although real estate represents a substantial proportion of the UK investment market, research in this area is extremely limited. This is particularly true of the performance and…
Abstract
Although real estate represents a substantial proportion of the UK investment market, research in this area is extremely limited. This is particularly true of the performance and construction of portfolios. This paper deals with one of the major issues which confronts both investor and advisor; namely, how effective is the diversification of a real estate portfolio as more properties are included. The analysis is undertaken at an empirical level and draws on similar research developed in the stock market. The main findings are that the low correlation between returns on individual properties enable high levels of risk reduction to be achieved. This correlation structure does, however, impose a penalty making it extremely difficult to construct highly diversified portfolios. The problem is exacerbated by the indivisibility of real estate assets.
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Tom Pfefferkorn, Julian Randall and Florian Scheuring
This chapter explores the impact of equality, diversity, and inclusivity (EDI) on internal change agents’ (ICAs) personal and professional development. We have surveyed 117 ICAs…
Abstract
This chapter explores the impact of equality, diversity, and inclusivity (EDI) on internal change agents’ (ICAs) personal and professional development. We have surveyed 117 ICAs that undergo a four-year digital development programme at Edinburgh Business School (EBS). Our survey design draws from expectancy, surprise, sensemaking, and attribution theories to test four hypotheses using Spearman’s rank. We found that diversity features such as gender, age, sector affiliation, work experience, management responsibility, and programme stage do not strongly impact ICAs’ experience of personal and professional development. Surprisingly, some diversity features had a modest or moderate impact on ICAs’ experience of personal and professional development. This disconfirmed our basic assumption about the effectiveness of inclusivity practices in the digital development programme at EBS. We conclude that future research should further investigate the impact of evaluation on ICAs’ personal and professional development and how we can secure it in a digital Business School context.
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The purpose of this study is twofold. First, the author posits and finds a significant positive relation between environmental performance (i.e., environmental efficiency) and…
Abstract
The purpose of this study is twofold. First, the author posits and finds a significant positive relation between environmental performance (i.e., environmental efficiency) and firm performance (i.e., firm efficiency) by using a large panel sample from 1987 to 2015. The results are consistent with the notion in prior research (e.g., Porter, 1991; Porter & van der Linde, 1995) that pollution indicates a form of resource inefficiency and reducing pollution can increase firm performance. Second, managerial ability has recently received tremendous research attention. The author investigates the impact of managerial ability on the relation between environmental efficiency and firm efficiency and finds that the results are mainly driven by firms with low managerial ability.
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Xuanli Xie, Jeffrey J. Reuer and Elko Klijn
Despite the growing interest in IJVs and their governance, systematic research is limited on the board of directors and their roles in international joint ventures in emerging…
Abstract
Despite the growing interest in IJVs and their governance, systematic research is limited on the board of directors and their roles in international joint ventures in emerging markets. In this study, we draw from corporate governance research that suggests that the levels of control and collaboration by boards are influenced by organizational complexity. While joint ventures possess several similarities compared to unitary firms, they also have unique sources of complexity given the fact that two or more international partners collaborate within JVs under an incomplete contract. Based on a sample of 114 IJVs, we argue and show four separate conditions that influence the functions that boards undertake as well as how control and collaboration as two separate functions are interrelated. Our findings address calls for research to open the black box of what boards actually do as well as to bring corporate governance theory to new organizational forms such as joint ventures.