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The purpose of this paper is to investigate the dependence of dark current on threading dislocations (TDs) in relaxed Ge layer for Ge/Si heterojunction photodetectors.

The analysis of the effects of TDs is based on SRH generation and recombination mechanism used in two‐dimensional drift‐diffusion numerical simulation.

It is found that the TDs in Ge layer acting as the recombination centers lead to large dark current densities of devices, and the recombination rate is affected by the impurity out‐diffusion from Si substrate. Besides, the TDs, being the acceptor‐like defects simultaneously, form band barrier at Si/Ge interface with lightly doped Si substrates, thus limiting the minority carrier transport and resulting in low dark current densities.

The simulation results are excellently consistent with the experimental data and indicate that the reduction of threading dislocation densities (TDDs), especially in Ge buffer layer, dramatically decreases dark currents densities of Ge/Si photodetectors. The investigation can be applied to imbue devices with desired characteristics.

The purpose of this paper is to focus on the relationship between the corporate governance (CG) environment, governance behavior and governance performance and place it into the research of the evolution and revolution of the Chinese telecommunication industry, a complex economic system.

On the basis of information about the Chinese telecommunication industry from 1949 to 2009 and China Mobile from 1997 to 2007, the authors analyze the Chinese telecommunication industry from two levels, namely industry level and firm level, with the combination of a critical incident method and time series method.

The results demonstrate that the evolution of the Chinese CG environment and governance behavior presents characteristics of punctuated equilibrium; governance behavior lags behind governance environment and it can cause the governance environment to evolve to benefit its development through exerting active effects; governance environment and governance performance strongly relate to and have an effect on each other; and the improvement of governance structure and mechanisms, together with the enhancement of strategic capability, can greatly contribute to the governance performance of firms.

This paper has divided the development of the Chinese telecommunication industry into four stages, revealing the relationship between governance environment, governance behavior and governance performance with a case study of the Chinese telecommunication industry and China Mobile.

Delivering messages and information to potentially interested users is one of the distinguishing applications of online enterprise social network (ESN). The purpose of this paper is to provide insights to better understand the repost preferences of users and provide personalized information service in enterprise social media marketing.

It is accomplished by constructing a target audience identification framework. Repost preference latent Dirichlet allocation (RPLDA) topic model topic model is proposed to understand the mass user online repost preferences toward different contents. A topic-oriented preference metric is proposed to measure the preference degree of individual users. And the function of reposting forecasting is formulated to identify target audience.

The empirical research shows the following: a total of 20 percent of the repost users in ESN represent the key active users who are particularly interested in the latent topic of messages in ESN and fits Pareto distribution; and the target audience identification framework can successfully identify different target key users for messages with different latent topics.

The findings should motivate marketing managers to improve enterprise brand by identifying key target audience in ESN and marketing in a way that truthfully reflects personalized preferences.

This study runs counter to most current business practices, which tend to use simple popularity to seek important users. Adaptively and dynamically identifying target audience appears to have considerable potential, especially in the rapidly growing area of enterprise social media information service.

An increasing number of users are inspired by enterprises to repost social media messages, which greatly contributes to the dissemination of such messages in an online social network. The purpose of this paper is to discover the repost patterns of users regarding enterprise social media messages to help enterprises improve information management abilities for social media.

This paper proposes a novel method to discover the repost patterns of users in enterprise social networking (ESN) at the macro-level through topic analysis. Specifically, it proposes the message-diversity metric to measure the latent topic diversity degree of the social media messages. Through this technique, the paper analyzes the message-diversity characteristics of the enterprise social media messages and then explores the repost patterns of users.

The experimental results show that a high repost rate is more prominent for the messages with diverse latent topics, where message-diversity is as high as 0.5.

The findings have great potential in several management areas, such as employing social media marketing, predicting popular messages, helping enterprises strengthen their online presence, and gathering more potential customers.

This study explores how the repost patterns of users in ESN can be determined through general macro-level behavior of users instead of their micro-level processes. The patterns can also lead to a deeper understanding of which contents can drive people to diffuse information. This study gives an important insight into the information behavior of social media users for enterprise management researchers.

The purpose of this study is to examine the impact of service quality, perceived usefulness and users’ cognitive satisfaction to determine the third-generation (3G) mobile phone users’ behavioural retention in using 3G telecommunications services.

A total of 243 valid questionnaires were collected from 3G users in the Klang Valley, Malaysia. The combination of partial least squares (PLS) path modelling approach and structural equation modelling (SEM; PLS-SEM) technique was used to analyze the measurement and structural model.

Our empirical assessment supports the proposed research hypotheses and further suggests that service quality is a second-order reflective construct comprising navigation and visual design, management and customer service and system reliability and connection quality.

Prior studies have examined the impact of service quality, perceived usefulness, overall users’ satisfaction and behavioural intention on an information system in general. This study is among the few studies that have attempted to gain insights into 3G users’ post-adoption experience with telecommunications services.

The purpose of this paper is to investigate the impact of political instability at the local level on foreign firms in China. Building on the literature on political embeddedness and business power, the authors propose a theoretical framework to explain how political turnover can affect foreign firms’ performance and how they respond to such challenges by leveraging their power bases.

To test the hypotheses, the authors apply fixed effects regression to an unbalanced panel data set comprising 13,360 foreign firms from 1998 to 2013 and the political replacement that involved changes in provincial governors.

The findings confirm that political turnover incidents have a negative impact on the performance of foreign firms in China. However, the authors also found that this negative relationship is weaker for firms that can choose various types of power sources. Specifically, the study reveals that foreign firms with large firm size, government ownership and a strong foreign direct investment community are better qualified to mitigate the negative effects of political instability.

This study contributes to the literature by developing the understanding of how political uncertainties and risks affect the performance of foreign firms in China and the importance of firms’ power in counterbalancing these effects. The research provides valuable insights into how multinational corporations can exploit their power to manage the effects of local political turnover, which has practical implications for the strategy and management of foreign firms operating in China.

Learning from past construction accident reports is critical to reducing their occurrence. Digital technology provides feasibility for extracting risk factors from unstructured reports, but there are few related studies, and there is a limitation that textual contextual information cannot be considered during extraction, which tends to miss some important factors. Meanwhile, further analysis, assessment and control for the extracted factors are lacking. This paper aims to explore an integrated model that combines the advantages of multiple digital technologies to effectively solve the above problems.

A total of 1000 construction accident reports from Chinese government websites were used as the dataset of this paper. After text pre-processing, the risk factors related to accident causes were extracted using KeyBERT, and the accident texts were encoded into structured data. Tree-augmented naive (TAN) Bayes was used to learn the data and construct a visualized risk analysis network for construction accidents.

The use of KeyBERT successfully considered the textual contextual information, prompting the extracted risk factors to be more complete. The integrated TAN successfully further explored construction risk factors from multiple perspectives, including the identification of key risk factors, the coupling analysis of risk factors and the troubleshooting method of accident risk source. The area under curve (AUC) value of the model reaches up to 0.938 after 10-fold cross-validation, indicating good performance.

This paper presents a new machine-assisted integrated model for accident report mining and risk factor analysis, and the research findings can provide theoretical and practical support for accident safety management.

Based on media choice theories and real option theory, this study aims to explore the role of communication media portfolio in achieving collaborative innovation with suppliers.

Using data of the Study of Competitiveness, Technology and Firm Linkages 2002 collected by the World Bank and the Enterprise Survey Organization of the Chinese National Bureau of Statistics, this study employed logit model to test the hypotheses. Moreover, some robustness analyses were conducted.

This study suggests that media multiplicity increases the probability for a firm to achieve collaborative innovation with suppliers on the basis of real option theory. Furthermore, distance from the suppliers and relationship formality make media multiplicity further important in collaborative innovation with suppliers.

Through investing how communication media portfolio affecting collaborative innovation with suppliers and the contingency factors, this study complements existing studies just concentrating on supplier capabilities, relationships with suppliers and characteristics of supplier network. Moreover, this study contributes in the information system field by exploring the role of media portfolio rather than individual media.

Laser point clouds are a 3D reconstruction method with wide range, high accuracy and strong adaptability. Therefore, the purpose is to discover a construction point cloud extraction method that can obtain complete information about the construction of rebar, facilitating construction quality inspection and tunnel data archiving, to reduce the cost and complexity of construction management.

Firstly, this paper analyzes the point cloud data of the tunnel during the construction phase, extracts the main features of the rebar data and proposes an M-E-L recognition method. Secondly, based on the actual conditions of the tunnel and the specifications of Chinese tunnel engineering, a rebar model experiment is designed to obtain experimental data. Finally, the feasibility and accuracy of the M-E-L recognition method are analyzed and tested based on the experimental data from the model.

Based on tunnel morphology characteristics, data preprocessing, Euclidean clustering and PCA shape extraction methods, a M-E-L identification algorithm is proposed for identifying secondary lining rebars in highway tunnel construction stages. The algorithm achieves 100% extraction of the first-layer rebars, allowing for the three-dimensional visualization of the on-site rebar situation. Subsequently, through data processing, rebar dimensions and spacings can be obtained. For the second-layer rebars, 55% extraction is achieved, providing information on the rebar skeleton and partial rebar details at the construction site. These extracted data can be further processed to verify compliance with construction requirements.

This paper introduces a laser point cloud method for double-layer rebar identification in tunnels. Current methods rely heavily on manual detection, lacking objectivity. Objective approaches for automatic rebar identification include image-based and LiDAR-based methods. Image-based methods are constrained by tunnel lighting conditions, while LiDAR focuses on straight rebar skeletons. Our research proposes a 3D point cloud recognition algorithm for tunnel lining rebar. This method can extract double-layer rebars and obtain construction rebar dimensions, enhancing management efficiency.

The purpose of this paper is to analyze the mechanism and filter efficacy of accumulation generation operator (AGO)/inverse accumulation generation operator (IAGO) in the frequency domain.

The AGO/IAGO in time domain will be transferred to the frequency domain by the Fourier transform. Based on the consistency of the mathematical expressions of the AGO/IAGO in the gray system and the digital filter in digital signal processing, the equivalent filter model of the AGO/IAGO is established. The unique methods in digital signal processing systems “spectrum analysis” of AGO/IAGO are carried out in the frequency domain.

Through the theoretical study and practical example, benefit of spectrum analysis is explained, and the mechanism and filter efficacy of AGO/IAGO are quantitatively analyzed. The study indicated that the AGO is particularly suitable to act on the system's behavior time series in which the long period parts is the main factor. The acted sequence has good effect of noise immunity.

The AGO/IAGO has a wonderful effect on the processing of some statistical data, e.g. most of the statistical data related to economic growth, crop production, climate and atmospheric changes are mainly affected by long period factors (i.e. low-frequency data), and most of the disturbances are short-period factors (high-frequency data). After processing by the 1-AGO, its high frequency content is suppressed, and its low frequency content is amplified. In terms of information theory, this two-way effect improves the signal-to-noise ratio greatly and reduces the proportion of noise/interference in the new sequence. Based on 1-AGO acting, the information mining and extrapolation prediction will have a good effect.

The authors find that 1-AGO has a wonderful effect on the processing of data sequence. When the 1-AGO acts on a data sequence X, its low-pass filtering effect will benefit the information fluctuations removing and high-frequency noise/interference reduction, so the data shows a clear exponential change trends. However, it is not suitable for excessive use because its equivalent filter has poles at the non-periodic content. But, because of pol effect at zero frequency, the 1-AGO will greatly amplify the low-frequency information parts and suppress the high-frequency parts in the information at the same time.