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Discusses the 6th ITCRR, its breadth of textile and clothing research activity, plus the encouragement given to workers in this field and its related areas. States that, within the newer research areas under the microscope of the community involved, technical textiles focuses on new, ‘smart’ garments and the initiatives in this field in both the UK and the international community at large. Covers this subject at length.

Total factor productivity (TFP) change is an important driver of long-run economic growth in the construction sector. However, examining TFP alone is insufficient to identify the cause of TFP changes. Therefore, this paper employs the infrequently used Geometric Young Index (GYI) and stochastic frontier analysis (SFA) to measure and decompose the TFP Index (TFPI) at the firm-level from 2009 to 2018 based on Malaysian construction firms' data.

To improve the TFPI estimation, normally unobserved environmental variables were included in the GYI-TFPI model. These are the physical operation of the firm (inland versus marine operation) and regional locality (West Malaysia versus East Malaysia). Consequently, the complete components of TFPI (i.e. technological, environmental, managerial, and statistical noise) can be accurately decomposed.

The results reveal that TFP change is affected by technological stagnation and improvements in technical efficiency but a decline in scale-mix efficiency. Moreover, the effect of environmental efficiency on TFP is most profound. In this case, being a marine construction firm and operating in East Malaysia can reduce TFPI by up to 38%. The result, therefore, indicates the need for progressive policies to improve long-term productivity.

Monitoring and evaluating productivity change allows an informed decision to be made by managers/policy makers to improve firms' competitiveness. Incentives and policies to improve innovation, competition, training, removing unnecessary taxes and regulation on outputs (inputs) could enhance the technological, technical and scale-mix of resources. Furthermore, improving public infrastructure, particularly in East Malaysia could improve regionality locality in relation to the environmental index.

This study contributes to knowledge by demonstrating how TFP components can be completely modelled using an aggregator index with good axiomatic properties and SFA. In addition, this paper is the first to apply and include the GYI and environmental variables in modelling construction productivity, which is of crucial importance in formulating appropriate policies.

This study investigates the convergence-of-interest and entrenchment effects on the relationship between managerial ownership and financial distress using evidence from the Chinese stock market. It also analyzes whether the relationship is mediated by research and development (R&D) investment.

Using a dataset consisting of 19,059 firm-year observations of Chinese listed companies in the Shanghai and Shenzhen Stock Exchanges between 2010 and 2020, this study employs both piecewise and curvilinear models.

The results indicate that managerial ownership has a negative association with firm financial distress in both the low (below 12%) and high (above 18%) convergence-of-interest regions of managerial ownership, suggesting that managerial ownership in this region may contribute to improve firm financial status. Meanwhile, managerial ownership has a positive association with firm financial distress in the entrenchment region (12–18%), implying that managerial ownership in the entrenchment region may contribute to impair firm financial status. Furthermore, the results show that R&D investment mediates the association between managerial ownership and financial distress.

This study is the first to provide evidence of a nonlinear relationship between managerial ownership and financial distress, and identify the entrenchment region in the Chinese setting.

The purpose of this paper is to explain why Singapore is a success story today despite the fact that its prospects for survival were dim when it became independent in August 1965.

This paper describes the changes in Singapore’s policy context from 1959 to 2016, analyses the five factors responsible for its success and concludes with advice for policy makers interested in implementing Singapore-style reforms to solve similar problems in their countries.

Singapore’s success can be attributed to these five factors: the pragmatic leadership of the late Lee Kuan Yew and his successors; an effective public bureaucracy; effective control of corruption; reliance on the “best and brightest” citizens through investment in education and competitive compensation; and learning from other countries.

This paper will be useful to those scholars and policy makers interested in learning from Singapore’s success in solving its problems.

This research investigates the differential impact of information technology (IT) investments based on their features, such as investment in data management capability, security improvement, IT outsourcing or new IT infrastructure. The Long-Horizon Event Study (LHES) is essential for providing a more appropriate measure of the value of IT investments because firms' strategic decisions often set long-horizon and large-scale organizational goals, and there is inherent uncertainty regarding future cash flows resulting from these investments. Therefore, the authors aim to analyze how announcements of IT investments affect the firm's abnormal stock returns over the long term and to compare the differential impact of different features of IT investment.

The authors gathered IT investment announcements and stock data of listed firms in Korea between 2000 and 2018, and the monthly stock market returns over the 5 years after the announcements. To measure the differential impact of IT investments based on the investment features, the authors separate announcements data into five groups. A LHES is used to estimate the long-term effects of IT investment announcements.

The results indicate that announcements of IT investments had a long-term positive effect on firm performance. Additionally, the findings reveal differential effects of IT investments across industries and investment features. Notably, news of self-developed IT investments and IT investments in the manufacturing industry had significantly positive effects. However, contrary to common belief, announcements of investments in so-called essential IT areas such as data, security, or new IT infrastructure did not yield significant effects.

Although the need for LHES has been emphasized in information systems research, few follow-up studies have been conducted since Barua and Mani (2018). This is primarily due to the challenges associated with collecting large-scale abnormal stock returns data over a long horizon. This research represents the first LHES to investigate the differential impact of IT investments based on their features. By doing so, this study can provide valuable insights for decision-makers within firms, helping them understand the time horizon of market outcomes of IT investments based on their features. Furthermore, this work extends the scope of LHES to comprehend the differential impacts of investment features. For instance, managers need to grasp that so-called essential IT investments, such as data management, security enhancements or new IT infrastructure, may not necessarily generate long-term market value.

The advent of derivatives and structured products has coincided with a proliferation of fixed income models used to analyze hedging, pricing, forecasting, and estimation for the term structure of interest rates. This article evaluates five models Ho‐Lee (HL); Black‐Derman‐Toy (BDT); Vasicek; Cox‐Ingersoll‐Ross (CIR); and Heath‐Jarrow‐Morton (HJM) (see Exhibit 1) that are currently used by structured finance practitioners. We suggest which models are most appropriate for assets with different time horizons, interest rate sensitivities and cashflow properties. The authors link model selection to structured financial instruments with the singular focus on the trade‐off between model precision/complexity and calculation costs.

Mixed recirculatory flow in the annuli of stationary and rotating horizontal cylinders were studied numerically. A set of distorted ‘false transient’ parameters were introduced to speed up the steady state solution of the unsteady vorticity, energy and stream function—vorticity equations. The inner cylinder of the annuli is assumed heated and rotating at Reynolds numbers that exclude the effects of centrifugal acceleration and three‐dimensional Taylor vortices. The Prandtl number considered is in the range of 0.01 to 1.0 and Rayleigh number in the range of 10² to 10⁶. Radius ratios of the cylinders considered are 1.25, 2.5 and 5.0. For a radius ratio of 2.5, inner cylinder rotation in the Reynolds number range of 0 to 1120 was considered. Vertical eccentricities in the range of ±2/3 were studied for cases of the rotating inner cylinder. Numerical experiments show that the mean Nusselt number increases with Rayleigh number for both cases of concentric and eccentric stationary inner cylinder. At a Prandtl number of order 1.0 with a fixed Rayleigh number, when the inner cylinder is made to rotate, the mean Nusselt number decreases throughout the flow. At lower Prandtl number of the order 0.1 to 0.01, the mean Nusselt number remained fairly constant with respect to the rotational Reynolds number.

The model for digitally controlled three-phase pulse width modulation (PWM) boost rectifiers is a sampled data model, which is different from the continuous time domain models presented in previous studies. The controller, which is tuned according to the model in continuous time domain and discretized by approximation methods, may exhibit some unpredictable performances and even result in unstable systems under some extreme situations. Consequently, a small-signal discrete-time model of digitally controlled three-phase PWM boost rectifier is required. The purpose of this paper is to provide a simple but accurate small-signal discrete-time model of digital controlled three-phase PWM boost rectifier, which explains the effect of the sampling period, modulator and time delays on system dynamic and improves the control performance.

Based on the Laplace domain analysis and the waveforms of up-down-count modulator, the small signal model of digital pulse width modulation (DPWM) in the Laplace domain is presented. With a combination of state-space average and a discrete-time modeling technique, a simplified large signal discrete time model is developed. With rotation transformation and feed-forward decoupling, the large-signal model is decoupled into a single input single output system with rotation transformation. Then, an integrated small signal model in the Laplace domain is constructed that included the time delay and modulation effect. Implementing the modified z-transform, a small-signal discrete-time model is derived from the integrated small signal model.

In a digital control system, besides the circuit parameters, the location of pole of open-loop transfer function is also related to system sampling time, affecting the system stability, and the time delay determines the location of the zero of open-loop transfer function, affecting the system dynamic. In addition to the circuit parameters discussed in previous literature, the right half plane (RHP) zero is also determined by the sampling period and the time delay. Furthermore, the corner frequency of the RHP zero is mainly determined by the sampling period.

The model developed in this paper, accounting for the effect of the sampling period, modulator and time delays on the system dynamic, give a sufficient insight into the behavior of the digitally controlled three-phase PWM rectifier. It can also explain the effect of sampling period and control delay time on system dynamic, accurately predict the system stability boundary and determine the oscillation frequency of the current loop in critical stable. The experimental results verify that the model is a simple and accurate control-oriented small-signal discrete-time model for the digitally controlled three-phase PWM boost rectifier.

This paper sets out to present a practical approach to develop an effective customer loyalty program by incorporating competition and heterogeneity in customers' preferences, and by avoiding the pitfalls associated with different types of loyalty programs.

To illustrate the approach, the paper presents a case study of T&T Supermarkets in Canada to show how a retailer can develop a cost‐effective customer loyalty program to retain and reward loyal customers so as to increase shopping frequency and shopping expenditure. The approach consists of four major steps, which are explained in detail.

Most T&T shoppers split their shopping trips at T&T (for Asian groceries and other specialty items) and a major competitor (for Western items). This creates a unique opportunity for T&T to develop a loyalty program that is intended to entice its loyal shoppers to increase their shopping frequency and expenditure at T&T. A “hybrid” reward structure was recommended to address the fact that there are two major segments of customers who prefer different types of loyalty rewards.

In addition to avoiding some common pitfalls of various loyalty programs, this paper presents a practical approach to develop an effective customer loyalty program by incorporating competition and heterogeneity in customers' preferences.

The purpose of this paper is to analyze the relationship between new product diffusion and consumer internet search patterns using big data and to investigate whether such data can be used in forecasting new product diffusion.

This research proposes a new product diffusion model based on the Bass diffusion model by incorporating consumer internet search behavior. Actual data from search engine queries and new vehicle sales for each vehicle class and region are used to estimate the proposed model. Statistical analyses are used to interpret the estimated results, and the prediction performance of the proposed method is compared with other methods to validate the usefulness of data for internet search engine queries in forecasting new product diffusion.

The estimated coefficients of the proposed model provide a clear interpretation of the relationship between new product diffusion and internet search volume. In 83.62 percent of 218 cases, analyzing the internet search pattern data are significant to explain new product diffusion and that internet search volume helps to predict new product diffusion. Therefore, marketing that seeks to increase internet search volume could positively affect vehicle sales. In addition, the demand forecasting performance of the proposed diffusion model is superior to those of other models for both long-term and short-term predictions.

As search queries have only been available since 2004, comparisons with data from earlier years are not possible. The proposed model can be extended using other big data from additional sources.

This research directly demonstrates the relationship between new product diffusion and consumer internet search pattern and investigates whether internet search queries can be used to forecast new product diffusion by product type and region. Based on the estimated results, increasing internet search volume could positively affect vehicle sales across product types and regions. Because the proposed model had the best prediction power compared with the other considered models for all cases with large margins, it can be successfully utilized in forecasting demand for new products.