Search results

This study compares the performance of two types of clustering methods, time-based and non-time-based clustering, in the identification of river discharge patterns at the Johor River basin during the northeast monsoon season. Time-based clustering is represented by employing dynamic time warping (DTW) dissimilarity measure, whereas non-time-based clustering is represented by employing Euclidean dissimilarity measure in analysing the Johor River discharge data. In addition, we combine each of these clustering methods with a frequency domain representation of the discharge data using Discrete Fourier Transform (DFT) to see if such transformation affects the clustering results. The clustering quality from the hierarchical data structures of the identified river discharge patterns for each of the methods is measured by the Cophenetic Correlation Coefficient (CPCC). The results from the time-based clustering using DTW based on DFT transformation show a higher CPCC value as compared to that of non-time-based clustering methods.

Routing protocols in wireless sensor networks (WSN) are a crucial challenge for which the goal is maximizing the system lifetime. Since the sensor nodes are with limited capabilities, these routing protocols should be simple, scalable, energy‐efficient, and robust to deal with a very large number of nodes, and also self‐configurable to node failures and changes of the network topology dynamically. The purpose of this paper is to present a new algorithm for cluster forming in WSN based on the node energy required to transmit to the base station.

Rotation selection of cluster‐head considering the remoteness of the nodes to the sink, and the network node residual energy.

The simulation results show that this algorithm allows network stability extension compared to the most known clustering algorithm.

Giving a probability to become cluster‐head based on the remoteness of the node to the sink.

The purpose of this paper is to assess the impact of open innovation practices on the innovation capability and export performance of UK small and medium‐sized enterprises (SMEs).

The empirical (quantitative) investigation is based on a sample of 64 SMEs in the UK – 33 “open” innovation firms and 31 “closed” innovation firms.

The overall results demonstrate that the international competitiveness of SMEs is highly dependent on the cumulative effects and interrelationship between two key internal components, i.e. R&D capacity and managerial structure and competencies, coupled with two external factors, i.e. open innovation practices and the ability of the firm to attract government grants for R&D and technological development.

Owing to the size of the sample, it has not been possible to undertake research within the context of specific regional disparities and/or sectoral characteristics.

In order to achieve and sustain competitive advantage in today's global market, SMEs need to collaborate with universities and other firms to advance and commercialise their technologies through “open innovation”.

Results show that open innovation activities and their impact on the international competitiveness of SMEs are complex and multi‐faceted. Essentially, they are highly related to and dependent upon the cumulative effects of, and interrelationship between, several key internal and external factors. Such factors cannot be fully explored through qualitative approaches as they require more complex and rigorous statistical analyses.

The purpose of this paper is to present competing risks models and show how dwell times can be applied to predict users’ online behavior. This information enables real-time personalization of web content.

This paper models transitions between pages based upon the dwell time of the initial state and then analyzes data from a web shop, illustrating how pages that are linked “compete” against each other. Relative risks for web page transitions are estimated based on the dwell time within a clickstream and survival analysis is used to predict clickstreams.

Using survival analysis and user dwell times allows for a detailed examination of transition behavior over time for different subgroups of internet users. Differences between buyers and non-buyers are shown.

As opposed to other academic fields, survival analysis has only infrequently been used in internet-related research. This paper illustrates how a novel application of this method yields interesting insights into internet users’ online behavior.

A key goal of any online retailer is to increase their customer conversation rates. Using survival analysis, this paper shows how dwell-time information, which can be easily extracted from any server log file, can be used to predict user behavior in real time. Companies can apply this information to design websites that dynamically adjust to assumed user behavior.

The method shows novel clickstream analysis not previously demonstrated. Importantly, this can support the move from web analytics and “big data” from hype to reality.

Examines the impact of the intensity level of Just‐in‐time (JIT) on quality. The intensity level of JIT is operationalized as a function of four variables: the existence of JIT as a timebased technology; the utilization of JIT to a certain extent; the integration of JIT with other time‐based technologies such as Group Technology (GT), Design for Manufacturability (DFM) and Total Quality Management (TQM); and the percentage of facilities that has been converted to using JIT. The term “quality” in this study is a composite construct of product quality, manufacturing workmanship, design and engineering quality, and vendor quality. This study was based on empirical data collected from 165 manufacturing firms in three industry groups in the USA: industrial machinery equipment, electronic and electric machinery equipment, and transport equipment. The two digit Standard Industrial Classification Codes (SIC) for these industry groups are 35, 36 and 37 respectively. The analysis of the results suggests that statistically significant differences in quality exist among firms with different intensity levels of JIT. The study has many implications for both academicians and practitioners.

Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity, scalability and interest drift that affect their performance. Despite the efforts made to solve these problems, there is still no RS that can solve or reduce all the problems simultaneously. Therefore, the purpose of this study is to provide an effective and comprehensive RS to solve or reduce all of the above issues, which uses a combination of basic customer information as well as big data techniques.

The most important steps in the proposed RS are: (1) collecting demographic and behavioral data of customers from an e-clothing store; (2) assessing customer personality traits; (3) creating a new user-item matrix based on customer/user interest; (4) calculating the similarity between customers with efficient k-nearest neighbor (EKNN) algorithm based on locality-sensitive hashing (LSH) approach and (5) defining a new similarity function based on a combination of personality traits, demographic characteristics and time-based purchasing behavior that are the key incentives for customers' purchases.

The proposed method was compared with different baselines (matrix factorization and ensemble). The results showed that the proposed method in terms of all evaluation measures led to a significant improvement in traditional collaborative filtering (CF) performance, and with a significant difference (more than 40%), performed better than all baselines. According to the results, we find that our proposed method, which uses a combination of personality information and demographics, as well as tracking the recent interests and needs of the customer with the LSH approach, helps to improve the effectiveness of the recommendations more than the baselines. This is due to the fact that this method, which uses the above information in conjunction with the LSH technique, is more effective and more accurate in solving problems of cold start, scalability, sparsity and interest drift.

The research data were limited to only one e-clothing store.

In order to achieve an accurate and real-time RS in e-commerce, it is essential to use a combination of customer information with efficient techniques. In this regard, according to the results of the research, the use of personality traits and demographic characteristics lead to a more accurate knowledge of customers' interests and thus better identification of similar customers. Therefore, this information should be considered as a solution to reduce the problems of cold start and sparsity. Also, a better judgment can be made about customers' interests by considering their recent purchases; therefore, in order to solve the problems of interest drifts, different weights should be assigned to purchases and launch time of products/items at different times (the more recent, the more weight). Finally, the LSH technique is used to increase the RS scalability in e-commerce. In total, a combination of personality traits, demographics and customer purchasing behavior over time with the LSH technique should be used to achieve an ideal RS. Using the RS proposed in this research, it is possible to create a comfortable and enjoyable shopping experience for customers by providing real-time recommendations that match customers' preferences and can result in an increase in the profitability of e-shops.

In this study, by considering a combination of personality traits, demographic characteristics and time-based purchasing behavior of customers along with the LSH technique, we were able for the first time to simultaneously solve the basic problems of CF, namely cold start, scalability, sparsity and interest drift, which led to a decrease in significant errors of recommendations and an increase in the accuracy of CF. The average errors of the recommendations provided to users based on the proposed model is only about 13%, and the accuracy and compliance of these recommendations with the interests of customers is about 92%. In addition, a 40% difference between the accuracy of the proposed method and the traditional CF method has been observed. This level of accuracy in RSs is very significant and special, which is certainly welcomed by e-business owners. This is also a new scientific finding that is very useful for programmers, users and researchers. In general, the main contributions of this research are: 1) proposing an accurate RS using personality traits, demographic characteristics and time-based purchasing behavior; 2) proposing an effective and comprehensive RS for a “clothing” online store; 3) improving the RS performance by solving the cold start issue using personality traits and demographic characteristics; 4) improving the scalability issue in RS through efficient k-nearest neighbors; 5) Mitigating the sparsity issue by using personality traits and demographic characteristics and also by densifying the user-item matrix and 6) improving the RS accuracy by solving the interest drift issue through developing a time-based user-item matrix.

Examines the impact of the intensity level of design for manufacturability (DFM) on the time‐to‐market. Hypothesizes that companies which use the DFM technology strategically are more likely than their counterparts to exhibit a shorter time‐to‐market, introduce new products and processes more often, and are more responsive to their customers needs. An index for measuring the intensity level of DFM is developed. The analysis was based on data collected from 165 manufacturing firms in three industry groups in US – industrial machinery equipment, electronic and electric machinery equipment, and transportation equipment. Suggests that differences in time‐to‐market among firms with different intensity level of DFM do exist. Shows the existence of moderate association between DFM and time‐to‐market, and between DFM and introducing new products and new processes.

The aim of this chapter is to investigate the context dependence of the implications of telework for work–family conflict. It examines whether and how the implications of telework for strain-based and time-based work–family conflict depend on work–family-supportive and high-demand workplace cultures. Based on a sample of 4,898 employees derived from a unique linked employer–employee study involving large organizations in different industries in Germany, multilevel fixed-effects regressions were estimated.

The results show that telework is associated with perceived higher levels of both time-based and strain-based work–family conflict, and that this is partly related to overtime work involved in telework. However, teleworkers experience higher levels of work–family conflict if they perceive their workplace culture to be highly demanding, and lower levels if supervisor work–family support is readily available.

Future research is required to investigate how the conclusions from this research vary between heterogonous employees and how work–family-supportive and high-demand workplace cultures interrelate in their implications on the use of telework for work–family conflict.

The findings show how important it is to implement telework in a way that not only accommodates employers’ interest in flexibilization, but that it also makes it possible to reconcile work with a family life that involves high levels of responsibility.

This is the first study which examines whether telework is either a resource that reduces or a demand that promotes work–family conflict by focusing on whether this depends on perceived workplace culture.

The strategic aim of this paper is to investigate whether time‐based competition (TBC) strategies are related to cultural aspects. In addition, the influences of company decision‐making and the success of competitive strategies in diverse globalised markets will be examined. Based on a multicultural perspective, the way time is considered depends on different assumptions among countries and cultures, as well as organisational patterns of decision‐making for a variety of business areas and services. TBC and its relationship to logistics and multiculturalism through the international benchmarking of furniture companies are then explored.

TBC and cultural perceptions in furniture manufacturing companies located in Brazil and Scandinavia were investigated. Data were collected from furniture companies by questionnaires and were analysed through descriptive statistics and multivariate techniques. The research presented here is part of a larger study that focuses on TBC strategies and multiculturalism in the furniture industry.

Data were collected from furniture companies located in different countries. The findings illustrate that there is a correlation between TBC strategies and cultural variables, as well as between TBC and seeking for local partnerships.

The originality of the paper lies in addressing relationships between TBC strategies, cultural aspects and the role of partnerships to improve logistics management competitiveness. To date, this area has received little attention in the literature. Likewise, the results point to partnerships being necessary to improve TBC and logistics strategies.