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The purpose of this paper is to establish and implement a direct topological reanalysis algorithm for general successive structural modifications, based on the updating matrix triangular factorization (UMTF) method for non-topological modification proposed by Song et al. [Computers and Structures, 143(2014):60-72].

In this method, topological modifications are viewed as a union of symbolic and numerical change of structural matrices. The numerical part is dealt with UMTF by directly updating the matrix triangular factors. For symbolic change, an integral structure which consists of all potential nodes/elements is introduced to avoid side effects on the efficiency during successive modifications. Necessary pre- and post processing are also developed for memory-economic matrix manipulation.

The new reanalysis algorithm is applicable to successive general structural modifications for arbitrary modification amplitudes and locations. It explicitly updates the factor matrices of the modified structure and thus guarantees the accuracy as full direct analysis while greatly enhancing the efficiency.

Examples including evolutionary structural optimization and sequential construction analysis show the capability and efficiency of the algorithm.

This innovative paper makes direct topological reanalysis be applicable for successive structural modifications in many different areas.

Online social networks are increasingly important venues for businesses to promote their products and image. However, information propagation in online social networks is significantly more complicated compared to traditional transmission media such as newspaper, radio, and television. In this chapter, we will discuss research on modeling and forecasting diffusion of virally marketed content in social networks. Important aspects include the content and its presentation, the network topology, and transmission dynamics. Theoretical models, algorithms, and case studies of viral marketing will be explored.

The purpose of this study is to propose an integrated model based on expectation–confirmation model (ECM), flow theory and human–organization–technology fit framework to examine whether human, organizational and technology factors as antecedents to medical professionals' beliefs can affect their continuance intention of the cloud-based e-learning system.

Sample data for this study were collected from medical professionals at five hospitals in Taiwan. A total of 500 questionnaires were distributed, and 368 (73.6%) useable questionnaires were analyzed using structural equation modeling in this study.

Synthetically speaking, human, organizational and technology factors, as antecedents to medical professionals' continuance intention of the cloud-based e-learning system have been examined, and the results strongly support the research model with all hypothesized links being significant.

Particularly, it is worth mentioning that the application of capturing both ECM and flow theory for completely explaining three types of factors (i.e. human, organizational and technology factors) as external variables to medical professionals' cloud-based e-learning continuance intention is well documented, that is, information systems (IS) and nonIS determinants are simultaneously evaluated, and extrinsic and intrinsic motivators are both taken into consideration in this study's theoretical development of medical professionals' cloud-based e-learning continuance intention to acquire a more comprehensive and robust analysis.

The main purpose of this study was to combine the extended technology acceptance model (TAM) with the innovation diffusion theory (IDT) to examine how learners' beliefs affected their usage intention of mobile learning (m-learning) and explore whether the relationships between learners' beliefs and their usage intention of m-learning changed under different levels of personal innovativeness regarding the new information technology (IT).

Sample data for this study were collected from Taiwanese mobile phone users, a total of 750 questionnaires were distributed, and 486 usable questionnaires were analyzed in this study, with a usable response rate of 64.80 per cent. Collected data were analyzed using structural equation modeling, multiple group analysis, and hierarchical moderated regression analysis.

Perceived usefulness (PU), perceived ease of use (PEOU), perceived enjoyment (PE), and compatibility can play essential roles in affecting learners' intention to use m-learning. Personal innovativeness can moderate the effects of PU, PEOU, and compatibility on intention to use m-learning except the effect of PE on intention to use m-learning.

Based on the views of the extended TAM with the IDT, this study incorporates intrinsic motivator (i.e. PE) along with conventional extrinsic motivators (i.e. PU and PEOU) into its analysis of m-learning acceptance for a more robust analysis and exhibits explicit results indicating that the effects of learners' beliefs on their usage intention of m-learning depend largely on their innovative predisposition regarding the new IT.

The purpose of this paper is to develop an evaluation model for National Judiciary Informatics System (NJIS), which is an e-justice system forming part of e-government, based on the models and the theories of information systems (ISs).

The survey was conducted on 8,840 internal users working for judicial services in Turkey. The success of the NJIS as an e-justice system is evaluated using structural equation modeling (SEM).

The results show that while the most important factor is the latent variable information quality in the SEM created to analyze the satisfaction of internal users using the NJIS, other factors include perceived usefulness (PU), system quality, and service quality, respectively. It is found that design quality has affected directly and positively the perceived ease of use (PEoU) while PEoU has affected the PU in the same manner.

This study was solely concerned with internal users. Therefore, a more comparative study in which other users such as lawyers and ordinary citizens can be incorporated is suggested. Related to internal user satisfaction of the e-justice system, it is explored whether or not internal users are satisfied with their information processing needs, the system’s efficiency, the number of process steps, technical office services, and the system in general.

The research presents a new developed evaluation model of e-justice system from an internal user perspective. Most evaluation models focus on system-centered evaluation or organizational structure while user-centered evaluation concerning judicial ISs has not been explored yet.

This study aims to develop a comprehensive adoption model that combines constructs from various theories and tests these theories against each other. The study combines a technology acceptance model, innovation diffusion theory and risk theory. It develops this model in a smart home applications context.

The study is based on an online survey consisting of 409 participants, and the data are analyzed using structural equation modeling.

Each theory provides unique insights into technology acceptance and numerous constructs are interrelated. Predictors from innovation diffusion and risk theory often display indirect effects through technology acceptance variables. The study identifies risk perception as a major inhibitor of use intention, mediated through perceived usefulness. Results reveal that the most important determinants of use intention are compatibility and usefulness of the application.

Studies which do not examine different theories together may not be able to detect the indirect effects of some predictors and could falsely conclude that these predictors do no matter. The findings emphasize the crucial role of compatibility, perceived usefulness and various risk facets associated with smart homes.

This study broadens the understanding about the necessity of combining acceptance and adoption drivers from several theories to better understand the usage of complex technological systems such as smart home applications.

The purpose of this research is to study the influence of self-service technologies (SST) on cross-buying and word-of-mouth. This study tests the direct impact of perceived usefulness and perceived ease of use on cross-buying and word-of-mouth. Further, this study also tests the mediating role of e-service quality for the aforementioned relationships. The study has been undertaken in the context of retail banking in an emerging market, India.

A cross-sectional survey research design was used to collect data from 235 customers of retail banks in India. The data were analysed using IBM AMOS 23.0 taking structural equation modelling (SEM) approach to test the hypothesised relationships.

The findings of the study suggest that both perceived usefulness and perceived ease of use influence cross-buying and word-of-mouth. E-service quality partially mediates the relationship between perceived usefulness on cross-buying and word-of-mouth. E-service quality partially mediates the relationship between perceived ease of use and cross-buying but does not mediate the relationship between perceived ease of use and word-of-mouth.

This study shows that managers need to focus on perceived usefulness and perceived ease of use in order to ensure cross-buying and promote word-of-mouth recommendations by customers. Also, managers will be able to enhance cross-buying and promote word-of-mouth recommendations if e-service quality delivered by the bank is perceived to be good by customers.

This study contributes to the literature on SST and offers empirical evidence to show that perceived usefulness and perceived ease of use can influence cross-buying as well as word-of-mouth. Previous studies have shown that SST usage can foster loyalty, and the present study offers new evidence on the outcomes of behavioural loyalty. The study has been undertaken through responses taken from retail bank customers in an emerging market. This study also contributes to the literature on SST by testing the mediating effect of e-service quality for the above-mentioned relationships.

The paper aims to provide a facile approach to the synthesis of polyurethane–silica nanocomposites by introducing self-made aqueous silica sols with different particle sizes into polyurethane materials. This paper investigates the effects of the silica nanoparticles on the polyester polyol, as well as the physical properties and transmittance of the hybrid polyurethane coatings.

Colloidal silica particles of different sizes were obtained using a sol–gel process and were then embedded into polyester polyol by in-situ polymerization. These polyester polyol–silica resins were synthesized using an azeotrope process, using xylene to remove the water generated in the system and present in the dispersion medium for the colloidal silica. The polyester polyol–silica resins were further cured using isocyanate trimers to form polyurethane–silica hybrid films.

The paper observed that the viscosity of the polyester polyol–silica nanocomposite resins increased and their appearance changed from transparent to ivory white as the particle size of the added silica was increased. It was found that increasing the hydroxyl content of the silica improved the film transmittance in the visible light region. However, the transmittance decreased sharply once the diameter of the silica particles reached 100 nm.

Because of the limitation of experimental conditions, some performances have not been tested. Therefore, researchers are encouraged to conduct further tests.

The paper provides a method of preparing hybrid polyurethane film by using silica; the results indicate that the introduction of nano-silica can improve the wear resistance and glass transition temperature of polyurethane coatings.

The results obtained in this study will be extremely useful to enhance the understanding of organic–inorganic hybrid materials.

Although digitalization in the workplace is burgeoning, tools are needed to facilitate personalized learning in informal learning settings. Existing knowledge recommendation techniques do not account for dynamic and task-oriented user preferences. The purpose of this paper is to propose a new design of a knowledge recommender system (RS) to fill this research gap and provide guidance for practitioners on how to enhance the effectiveness of workplace learning.

This study employs the design science research approach. A novel hybrid knowledge recommendation technique is proposed. An experiment was carried out in a case company to demonstrate the effectiveness of the proposed system design. Quantitative data were collected to investigate the influence of personalized knowledge service on users’ learning attitude.

The proposed personalized knowledge RS obtained satisfactory user feedback. The results also show that providing personalized knowledge service can positively influence users’ perceived usefulness of learning.

This research highlights the importance of providing digital support for workplace learners. The proposed new knowledge recommendation technique would be useful for practitioners and developers to harness information technology to facilitate workplace learning and effect organization learning strategies.

This study expands the scope of research on RS and workplace learning. This research also draws scholarly attention to the effective utilization of digital techniques, such as a RS, to support user decision making in the workplace.