Information Processing Demands Research Articles

Mapping drug smuggling networks in Japan: a social network analysis of trial documents

ABSTRACT This paper addresses a significant gap in drug market literature by examining high-level drug trafficking networks in Japan. We focus on three aspects: the structure of drug importation networks, the impact of transport methods on these structures, and the role of Japan’s mafia, the yakuza. Using novel statistical techniques that extend exponential random graph models (ERGMs) to multi-network samples, we analyse 573 Japanese trial documents on wholesale drug importation. We test the theory that trafficking operations with higher information-processing demands exhibit more efficiency-oriented network structures, while governance-type groups avoid expanding into trade activities. Our findings support these theories, showing that networks with higher information-processing demands are more efficiency-oriented but maintain security. Conversely, smaller networks with simpler transport methods prioritise security and concealment. Additionally, the yakuza do not organisationally engage in drug trafficking; when involved, yakuza members act as independent entrepreneurs.

New-Style Logic Operation and Neuromorphic Computing Enabled by Optoelectronic Artificial Synapses in an MXene/Y:HfO2 Ferroelectric Memristor.

Today's computing systems, to meet the enormous demands of information processing, have driven the development of brain-inspired neuromorphic systems. However, there are relatively few optoelectronic devices in most brain-inspired neuromorphic systems that can simultaneously regulate the conductivity through both optical and electrical signals. In this work, the Au/MXene/Y:HfO2/FTO ferroelectric memristor as an optoelectronic artificial synaptic device exhibited both digital and analog resistance switching (RS) behaviors under different voltages with a good switching ratio (>103). Under optoelectronic conditions, optimal weight update parameters and an enhanced algorithm achieved 97.1% recognition accuracy in convolutional neural networks. A new logic gate circuit specifically designed for optoelectronic inputs was established. Furthermore, the device integrates the impact of relative humidity to develop an innovative three-person voting mechanism with a veto power. These results provide a feasible approach for integrating optoelectronic artificial synapses with logic-based computing devices.

A CsPbBr3/CdS-based hybrid bidirectional optoelectronic device with light-emitting, modulation, and detection functions

Optoelectronic integrated circuits, with a broad photonic transportation bandwidth, have emerged as a promising solution to fulfill the escalating demands for high-volume information transportation and processing. However, challenges persist in developing optoelectronic integrated circuits based on low-dimensional nanostructures, including limited integration density and high energy consumption. Here, we demonstrate a bidirectional optoelectronic device by integrating a light-emitting/harvesting CsPbBr3 nanoplate with a waveguiding/modulating/detecting CdS nanobelt. By configuring the CsPbBr3 nanoplate in a Schottky-type device structure with a metal electrode, bright electroluminescence was attained at a bias voltage of 18 V. Thanks to the electric field-tuned phonon-coupling effect, the waveguided light in the CdS nanobelt exhibited a high modulation depth of up to 94%, rendering it an excellent building block as optical modulators and optical switches. Moreover, the integrated nanostructure device showcased functionality in the photodetection mode. The proposed device architecture holds promise for broader applications, potentially extending to other perovskite-coupled II–VI semiconductor optoelectronic integrated circuits for expanding integration capacity and enhancing optoelectronic performance.

Visual stimulation by extensive visual media consumption can be beneficial for motor learning

In this randomized controlled intervention trial, we investigated whether intense visual stimulation through television watching can enhance visual information processing and motor learning performance. 74 healthy young adults were trained in a motor skill with visual information processing demands while being accommodated in a controlled environment for five days. The experimental manipulation (n = 37) consisted of prolonged television watching (i.e., 8 h/day, + 62.5% on average) to induce intense exposure to visual stimulation. The control group (n = 37) did not consume visual media. The groups were compared by motor learning performance throughout the study as well as pre/post visual attention parameters and resting-state network connectivity in functional MRI. We found that the intervention group performed significantly better in the motor learning task (+ 8.21% (95%-CI[12.04, 4.31], t(70) = 4.23, p < 0.001) while showing an increased capacity of visual short-term memory (+ 0.254, t(58) = − 3.19, p = 0.002) and increased connectivity between visual and motor-learning associated resting-state networks. Our findings suggest that the human brain might enter a state of accentuated visuomotor integration to support the implementation of motor learning with visual information processing demands if challenged by ample input of visual stimulation. Further investigation is needed to evaluate the persistence of this effect regarding participants exposed to accustomed amounts of visual media consumption.Clinical Trials Registration: This trial was registered in the German Clinical Trials Register/Deutsches Register klinischer Studien (DRKS): DRKS00019955.

Don't throw the associative baby out with the Bayesian bathwater: Children are more associative when reasoning retrospectively under information processing demands.

Causal reasoning is a fundamental cognitive ability that enables individualsto learn about the complex interactions in the world around them. However, the mechanisms that underpin causal reasoning are not well understood. For example, it remains unresolved whether children's causal inferences are best explained by Bayesian inference or associative learning. The two experiments and computational models reported here were designed to examine whether 5- and 6-year-olds will retrospectively reevaluate objects-that is, adjust their beliefs about the causal status of some objects presented at an earlier point in time based on the observed causal status of other objects presented at a later point in time-when asked to reason about 3 and 4 objects and under varying degrees of information processing demands. Additionally, the experiments and models were designed to determine whether children's retrospective reevaluations were best explained by associative learning, Bayesian inference, or some combination of both. The results indicated that participants retrospectively reevaluated causal inferences under minimal information-processing demands (Experiment 1) but failed to do so under greater information processing demands (Experiment 2) and that their performance was better captured by an associative learning mechanism, with less support for descriptions that rely on Bayesian inference. RESEARCH HIGHLIGHTS: Five- and 6-year-old children engage in retrospective reevaluation under minimal information-processing demands (Experiment 1). Five- and 6-year-old children do not engage in retrospective reevaluation under more extensive information-processing demands (Experiment 2). Across both experiments, children's retrospective reevaluations were better explained by a simple associative learning model, with only minimal support for a simple Bayesian model. These data contribute to our understanding of the cognitive mechanisms by which children make causal judgements.

Laminar compartmentalization of attention modulation in area V4 aligns with the demands of visual processing hierarchy in the cortex

Attention selectively enhances neural responses to low contrast stimuli in visual area V4, a critical hub that sends projections both up and down the visual hierarchy. Veridical encoding of contrast information is a key computation in early visual areas, while later stages encoding higher level features benefit from improved sensitivity to low contrast. How area V4 meets these distinct information processing demands in the attentive state is unknown. We found that attentional modulation in V4 is cortical layer and cell-class specific. Putative excitatory neurons in the superficial layers show enhanced boosting of low contrast information, while those of deep layers exhibit contrast-independent scaling. Computational modeling suggested the extent of spatial integration of inhibitory neurons as the mechanism behind such laminar differences. Considering that superficial neurons are known to project to higher areas and deep layers to early visual areas, our findings suggest that the interactions between attention and contrast in V4 are compartmentalized, in alignment with the demands of the visual processing hierarchy.

Transparent artificial synapses based on Ag/Al-doped ZnO/ITO memristors for bioinspired neuromorphic computing

Today's computing systems to meet the enormous demands of information processing have driven the development of brain-inspired neuromorphic systems that can perform fast and energy-efficient calculations. In this study, Al-doped ZnO (AZO: ZnO to Al2O3 = 98:2 wt%) films with over 95 % light transmission were prepared by RF magnetron sputtering, and a structurally simple Ag/AZO/ITO memristor was fabricated. Moreover, the sustained potentiation/depression behavior of the memristor allows it to mimic artificial synapses that exhibit biological synaptic and neuronal functions such as short-term/long-term plasticity and paired-pulse facilitation.These results will contribute to the research and development of high-performance artificial synapses.

Support Vector Machine based Automatic Classification Method for IoT big Data Features

&lt;p&gt;As China’s information technology development shifts from a single high-speed growth stage to a multidimensional high-quality development stage, the Internet of Things (IoT) enters all aspects of life and becomes more and more popular. The demand for IoT big data information analysis and processing is increasing, and the important role of feature automatic classification methods becomes increasingly prominent. This research proposes SPO-SVM and WSPO-SVM models based on support vector machine for smart home environment monitoring data under the big data of Internet of Things, and then optimizes them with particle swarm optimization algorithm and adaptive method. Finally, the data set is selected for comparative experimental analysis of each optimization algorithm model. The experimental results show that the optimized WSPO-SVM model has less total misclassification and single class misclassification compared with other algorithms under Wine dataset. In cross-validation, both its classification accuracy performance outperformed other algorithms. Under 10 sets of smart home environment monitoring data sets, the WSPO-SVM algorithm model achieves 100% accuracy in 6 out of 10 test data sets, with an average accuracy of 97.67%, which is about 9% higher than the ordinary SVM algorithm model and about 15% higher than other feature classification algorithms. The experimental results prove that the WSPO-SVM algorithm can complete the feature classification work in the IoT big data environment, which meets the expectation.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

The Mediating Effects of Entrepreneurial Orientation Between Procedural Rationality and Strategic Decision-making Effectiveness

Researchers have shown interest in theory of organizational information processing and the resource-based theory, as they can both serve to provide competitive advantages for companies. Earlier studies have indicated that procedural rationality is used by firms to increase the demand for information processing when making effective strategic decisions. However, there is limited understanding of the mediating effects of entrepreneurial orientation as an intangible resource for organizations between procedural rationality and strategic decision-making effectiveness in the available studies in the literature. Thus, this study sheds light on the relationship between procedural rationality in strategic decision-making from organizations and their strategic decision-making effectiveness, taking into consideration the mediating effects of entrepreneurial orientation as an intangible resource for organizations. The research model was tested using a structural equation modelling design based on survey data from 162 companies that work under the umbrella of the Saudi financial sector, collated and analysed by software (SPSS and SmartPLS), the research found that procedural rationality in strategic decision-making within organizations positively affected their strategic decision-making effectiveness. Likewise, the research revealed that procedural rationality in strategic decision-making increased the level of entrepreneurial orientation. Moreover, the research findings showed that the high level of entrepreneurial orientation within organizations positively impacted their strategic decision-making effectiveness. These findings enrich the fields of strategic decision-making and procedural rationality through entrepreneurial orientation within organizations, this relationship should synchronize in function focusing on relevant information, data analytics, and strategic approach process. In light of this, this study brings scientific evidence from the perspective of financial companies within Saudi Arabia.

When Do Boards of Directors Contribute to Shareholder Value in Firms Targeted for Acquisition? A Group Information-Processing Perspective

We draw on group information-processing theory to investigate how target boards of directors may contribute to target value capture during the private negotiations phase in acquisitions. We view target boards as information-processing groups and private negotiations as information-processing tasks. We argue that target board meeting frequency is associated with increased processing—gathering, sharing, and analyzing—of acquisition-related information, which improves target bargaining and, ultimately, target value capture. We further posit that this value-enhancing effect of target board meeting frequency is more pronounced when target board composition improves the ability of target boards to process acquisition-related information. Finally, we expect that meeting frequency is more consequential for target bargaining and value capture when acquisition complexity imposes high information-processing demands on the target boards during private negotiations. Empirical evidence from a sample of acquisitions of publicly listed firms in the United States offers support for our group information-processing perspective on board contribution to shareholder value in firms targeted for acquisition. Funding: This work was supported by the Strategy Research Foundation [Dissertation Grant SRF-2015DP-0016]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1643 .

Layered Gallium Monosulfide as Phase‐Change Material for Reconfigurable Nanophotonic Components On‐Chip

AbstractThe demand for information processing at ultrahigh speed with large data transmission capacity is continuously rising. Necessary building blocks for on‐chip photonic integrated circuits (PICs) are reconfigurable integrated low‐loss high‐speed modulators and switches. Phase change materials (PCMs) provide unique opportunities for integration into PICs. Here, the investigation of layered gallium monosulfide (GaS) as a novel low‐loss PCM from infrared to optical frequencies is pioneered, with high index contrast (Δn ≈0.5) at the optical telecommunication band. The GaS bandgap switches from 1.5 ± 0.2 eV for the amorphous state to 2.1 ± 0.1 eV for the crystalline state. It is demonstrated that the reversible GaS amorphous‐to‐crystalline phase transition can be operated thermally and by picosecond green (532 nm) laser irradiation. The design of a reconfigurable integrated optical modulator on‐chip based on Mach‐Zehnder Interferometers (MZI) with the GaS PCM cell deposited on one of the arms for application is presented at the telecommunication wavelength of λ = 1310 nm, where the standard single mode optical fiber exhibits zero chromatic dispersion, and at λ = 1550 nm, where a minimum optical loss of 0.22 dB km−1 is obtained. This opens the route to applications such as reconfigurable modulators, beam steering using phase modulation, and photonic neural networks.

Blind Post-Decision State-Based Reinforcement Learning for Intelligent IoT

Recent years have witnessed a renewed interest in reinforcement learning (RL) due to the rapid growth of the Internet-of-Things (IoT) and their associated intelligent information processing and decision-making demands. As the slow learning speed is one of the major stumbling blocks of the classic RL algorithms, substantial efforts have been devoted to developing faster RL algorithms. Among them, post-decision state (PDS) learning is a prominent one, which can often improve the learning speed by orders of magnitude by exploiting the structural property of the underlying Markov decision processes (MDPs). However, conventional PDS learning requires prior information about the PDS transition probability, which may not be always available in practice. To lift this limitation, a novel blind PDS (b-PDS) learning algorithm is proposed in this work by leveraging the generic two-timescale stochastic approximation framework. By introducing an extra estimating procedure about the PDS transition probability, b-PDS learning can achieve a similar improvement of learning speed as conventional PDS learning while excluding the need for prior information. In addition, by analyzing the globally asymptotically stable equilibrium of the corresponding ordinary differential equation (o.d.e.), the convergence and optimality of b-PDS learning are established. Moreover, extensive simulation results are provided to validate the effectiveness of the proposed algorithm. Over the considered random MDPs, it has been observed that, to reach 90% of the best possible time average reward, the proposed b-PDS learning can reduce the learning time by 70% compared to Q-learning and 30% compared to Dyna.

Preliminary evidence for progressions in ownership reasoning over the preschool period.

Defining developmental progressions can be an important step in identifying developmental precursors and mechanisms of change, within and across areas of reasoning. In one exploratory study, we examine whether the development of children's thinking about ownership follows a systematic progression wherein some components emerge reliably before others. We examine this issue in a sample of 72 children: 40 older 2-year-olds, Mage = 2.78 (.14); R = 2.50-3.00, and 32 older 4-year-olds, Mage = 4.77 (.16); R = 4.50-5.00, living in Michigan in the United States. We use a battery of four established ownership tasks that tested different aspects of children's ownership thinking. A Guttman test revealed a reliable sequence that explained 81.9% of children's performance. Namely, we discovered that identifying familiar owned objects emerged first, control of permission as a cue to ownership second, understanding ownership transfers third, and the tracking of sets of identical objects last. This ordering suggests two foundational ownership abilities on which more complex reasoning may be built: the ability to include information about familiar owners in children's mental models of objects and recognizing that control is central to ownership. The observed progression is an important first step toward developing a formal ownership scale. This study paves the way for mapping the conceptual and information-processing demands (e.g., executive functioning, memory) that likely underlie change in ownership thinking across childhood. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Harnessing Computational Complexity Theory to Model Human Decision-making and Cognition.

A central aim of cognitive science is to understand the fundamental mechanisms that enable humans to navigate and make sense of complex environments. In this letter, we argue that computational complexity theory, a foundational framework for evaluating computational resource requirements, holds significant potential in addressing this challenge. As humans possess limited cognitive resources for processing vast amounts of information, understanding how humans perform complex cognitive tasks requires comprehending the underlying factors that drive information processing demands. Computational complexity theory provides a comprehensive theoretical framework to achieve this goal. By adopting this framework, we can gain new insights into how cognitive systems work and develop a more nuanced understanding of the relation between task complexity and human behavior. We provide empirical evidence supporting our argument and identify several open research questions and challenges in applying computational complexity theory to human decision-making and cognitive science atlarge.

Utilizing Augmented Reality to Quantify Information Processing and Motor Performance in Military Personnel to Detect Mild Traumatic Brain Injury (P8-1.002)

<h3>Objective:</h3> Utilize augmented reality (AR) to create military specific scenarios that can be objectively quantified and used to facilitate the detection of and return-to-duty (RTD) following mild traumatic brain injury (mTBI). <h3>Background:</h3> Methods of detecting military mTBI are rooted in neuropsychological tests that measure a construct of executive or motor function. This approach does not lend itself to accurate detection or appropriate RTD. We have demonstrated the HoloLens 2 AR device provides accurate biomechanical data. Placing digital assets in a user’s real-world via AR technology creates an ecological environment to evaluate function. An AR Rifle qualification test (RQT) to evaluate dynamic movement, information processing, shooting performance and decision-making was developed. To evaluate performance in the context of a group or military unit, a room clearing assessment was created. <h3>Design/Methods:</h3> Data were gathered from 157 active-duty service members. The RQT was performed under single- and dual-task conditions. Five room clearing scenarios in which information processing and motor demands were manipulated were completed. Kinematic data from the HoloLens 2 provided biomechanical outcomes characterizing cognitive and motor performance. <h3>Results:</h3> Reaction time (RT) to firing and accuracy of target acquisition in the RQT did not differ between single- and dual-task conditions (<i>p&lt;0.05)</i>. Transition time from kneel to stand did not differ between conditions; however, dual-task sway path (41.2±17.5 cm) post-transition was increased compared to single-task (35.1±14.2 cm). There was a significant increase in processing time as the complexity of the room clearing scenarios increased. <h3>Conclusions:</h3> The objective quantification of information processing (RT) and motor (postural sway and movement time) function while performing ecological AR military tasks can enhance the detection of mTBI using objective functional metrics. The use of AR to deliver realistic digital experiences will facilitate appropriate RTD to ensure the safety of the service member and their unit. <b>Disclosure:</b> Mr. Kaya has nothing to disclose. Anson Rosenfeldt has received personal compensation for serving as an employee of Cleveland Clinic. Anson Rosenfeldt has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Enspire DBS. The institution of Anson Rosenfeldt has received research support from Michael J. Fox Foundation. The institution of Anson Rosenfeldt has received research support from National Institute of Health. The institution of Anson Rosenfeldt has received research support from Department of Defense. Anson Rosenfeldt has received intellectual property interests from a discovery or technology relating to health care. Mr. Waltz has nothing to disclose. Mrs. Jansen has nothing to disclose. Ms. Owen has nothing to disclose. Mrs. Dunlap has nothing to disclose. Karissa Hastilow has nothing to disclose. Mrs. Scelina has nothing to disclose. Mr. Zimmerman has nothing to disclose. Morgan McGrath has nothing to disclose. Amanda Penko has nothing to disclose. The institution of Dr. Alberts has received research support from NIH. Dr. Alberts has received intellectual property interests from a discovery or technology relating to health care. Dr. Alberts has received personal compensation in the range of $500-$4,999 for serving as a Member, Health and Wellness Council with Peloton Interactive.

Perfect nonreciprocity by loss engineering

Realization of nonreciprocal transmission with low insertion loss and high contrast simultaneously is in great demand for one-way optical communication and information processing. Here we propose a generic approach to achieving perfect nonreciprocity that allows lossless unidirectional transmission by engineering energy losses. The loss of the intermediate mode induces a phase lag impinging on the indirect channel for energy transmission, which does not depend on the energy transmission direction. When the direct transmission channel coexists with the indirect lossy transmission channel, the dual-channel interference can be tuned to be destructive simultaneously for backward transmission from the rightmost mode to the leftmost mode, and for forward transmission from the leftmost mode to the intermediate mode. The former interference outcome corresponds to 100% nonreciprocity contrast, and the latter guarantees zero insertion loss for forward transmission. Additionally, our scheme also allows a nonreciprocity response over a wide bandwidth by increasing the losses while keeping perfect nonreciprocity at resonance. The robustness against loss indicates that our scheme is advantageous in the implementation of nonreciprocal optical devices with high performance.

The Influence of Cell Phone Usage on Dynamic Stability of the Body During Walking.

Dual-task walking and cell phone usage, which is associated with high cognitive load and reduced situational awareness, can increase risk of a collision, a fall event, or death. The objective of this study was to quantify the effect of dual-task cell phone talking, texting, and reading while walking on spatiotemporal gait parameters; minimum foot clearance; and dynamic stability of the lower limb joints, trunk, and head. Nineteen healthy male participants walked on an instrumented treadmill at their self-selected speed as well as walking while simultaneously (1)reading on a cell phone, (2)texting, and (3)talking on a cell phone. Gait analyses were performed using an optical motion analysis system, and dynamic stability was calculated using the Maximum Lyapunov Exponent. Dual-task cell phone usage had a significant destabilizing influence on the lower limb joints during walking. Cell phone talking while walking significantly increased step width and length and decreased minimum foot clearance height (P < .05). The findings suggest that dual-task walking and cell phone conversation may present a greater risk of a fall event than texting or reading. This may be due to the requirements for more rapid information processing and cognitive demand at the expense of motor control of joint stability.

Robust multi-objective optimization for islanded data center microgrid operations

Electricity cost has become a critical concern of data center operations with the rapid increasing of information processing demand. Data center microgrid (DCMG) is a promising way to reduce electric energy consumption from traditional fossil fuel generators and the billing cost, by effectively utilizing local renewable energy, e.g., wind power. However, uncertainties of wind power generation and real-time workload of data center would have significant impacts on the operational efficiency of DCMG, especially when it is in the island mode. For this reason, a novel affinely adjustable policy based robust multi-objective optimization model under flexible uncertainty set is proposed in this paper, which simultaneously optimizes wind power curtailment, the operation cost, and the over-plus level of computation resource, while considering uncertainties of both the wind power and real-time workload. Through numerical simulation studies, the validity of robust multi-objective optimization model for the island operation of DCMG is verified. Besides, the effectiveness of the proposed methods, i.e., the novel affinely adjustable policy and the flexible uncertainty set, in handling uncertainties are evaluated. Compared to the conventional robust multi-objective optimization model, the proposed approach reduces the operating costs of about 10% in average while maintaining similar reliability in numerical simulations. Moreover, the complex quantitative relationship among these multiple objectives is further investigated. Simulation results indicate the minimization of wind power curtailment and over-plus level of computation resource increases about 25% of the operation cost. These quantitative relationships can well support the decision making of DCMG operation management.

Emotional Expression and Analysis in Music Performance Based on Edge Computing

The expression of emotion in music performance is the soul of music, and the emotion revealed by the performer during the performance can bring emotional resonance to the audience. The emotions expressed by music such as joy, anger, and sadness are the meaning of music’s existence. Music without emotion will be dead. However, the music itself has no emotion at all; it is just a regular sound, so the emotional reading of music performance is very important. Music performance is an interpretation of music, and it is the most important emotional information and communication medium for human beings. Through the appreciation of the works to express the author’s emotions, the different performance forms of musical instruments, dance, and singing bring emotional resonance to the audience. Edge computing is the core technology and edge node of the Internet of everything in the new era, and it is constantly innovating with the rapid development of computers and the great changes brought about by them. Nowadays, people’s demand for emotional information processing of music performances has also increased. The research attention to music performance and the attention to its application technology have also received unprecedented development, so the requirements for the ability of human and machine interaction are getting higher and higher. With the increasing maturity of multimedia and communication technologies, there is an increasing expectation of using computers to express human thoughts and emotions. By combining the two, Dongfeng’s expression and analysis of music emotions through edge computing have also ushered in new developments. For example, people upload or share music and dance videos to their friends through WeChat, QQ, Douyin, etc., which greatly enriches people’s emotional world. The analysis and judgment of music emotion are the main subject of the joint development of both musicology and psychological research. With the help of computer science technology and artificial intelligence and other tools, the purpose of music emotional research can also be achieved. Particularly with the advancement of science and technology and the vigorous development of computer application technology, people’s needs for emotional expression and analysis of music can now be carried out with the help of computers. However, the amount of data generated is extremely large, and using edge servers for data processing can improve the efficiency of analysis and processing to meet people’s needs.

Effects of Top-Down, Bottom-Up, and Horizontal Communication on Organizational Commitment: Evidence From Chinese Internet Firms

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</b> An organization's internal communication is an important factor shaping employees’ organizational commitment. Internal communication practices can be classified into three types according to the direction of information flow: top-down, bottom-up, and horizontal communication. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research questions:</b> 1. What are the relationships between the three types of internal communication and organizational commitment? 2. Are there any mediating routes that bridge the relationship between internal communication and organizational commitment? 3. Do the effects of internal communication on organizational commitment vary for different jobs? <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Literature review:</b> Based on the social exchange theory, we uncover the underlying mechanism of the relationships between the three types of internal communication and organizational commitment. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methodology:</b> A structural equation model using partial least squares was employed to analyze survey data from 12,817 full-time employees in the Chinese internet sector. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results and conclusions:</b> The results suggest that top-down, bottom-up, and horizontal communication affect organizational commitment positively and significantly via the mediating routes of employees’ perceived job attractiveness or perceived customer service performance. The study also reveals a fit between employees’ job characteristics (e.g., information-processing demands) and internal communication types to improve organizational commitment. These findings generate theoretical and practical implications for professional communication management.