Information Processing Theory Research Articles

Deconstructing the Complex Network of Student Satisfaction in Open and Distance Learning: An Application of Structural Equation Modeling

The rapid spread of open and distance learning (ODL) in higher education has sparked a strong interest in the determinants of student satisfaction. Although previous studies have explored various factors that influence satisfaction, a comprehensive model integrating multiple dimensions is still lacking. This study proposes and empirically tests a comprehensive structural equation model of ODL student satisfaction, incorporating interaction, instructor quality, learning management system (LMS), assessment, and student self-regulation as key predictors. Based on constructivist, collaborative, and cognitive information processing theories, we hypothesize direct effects and mediation relationships between these constructs. Data collected from 1,450 students at a large open university in China were analyzed using partial least squares structural equation modeling (PLS-SEM). The results reveal complex interrelationships among the predictors, with interaction and self-regulation emerging as key mediating variables. This study contributes to the ODL literature by providing a nuanced understanding of the satisfaction formation process and offers practical implications for improving the ODL experience. Our findings highlight the need for a holistic approach to ODL design and implementation, emphasizing the synergistic effects of technology, instructional, and personal factors in fostering student satisfaction.

Do supply chain finance and trade digitization matter in mitigating financial instability in supply chains? An information processing theory view

ABSTRACT This research is grounded in the information processing theory and aims to assess the impact of supply chain financial instability and supply chain management capabilities (e.g. supply chain finance and trade digitization) on the performance of a focal firm. In particular, we consider the impact of supply chain financial instability as information processing needs and supply chain finance and trade digitization as information processing capabilities. We discuss how the fit between the information processing capabilities and the information processing needs improves a firm’s performance, which results in competitive advantage. Path analyses were performed using data collected from 199 manufacturing enterprises in China. The results suggest that when supply chain finance and trade digitization are fitted with the needs to mitigate the impact of supply chain financial instability, a focal firm’s performance can be improved through reduced costs, enhanced efficiency, and bolstered market value. These value-adding outcomes would help gain competitive advantage.

Optimizing Business Processes: Harnessing Robotic Process Automation in Chinese Enterprises

Abstract: With the continuous development of science and technology, the study of digital technology in China has become increasingly in-depth. As an effective means for enterprises to achieve automated management and standardized operations, Robotic Process Automation (RPA) has attracted widespread attention. RPA process automation technology can improve the efficiency of financial management and ensure the quality of work while meeting enterprise development needs. This study explores the multifaceted applications and impact of RPA in the financial shared services sector. By utilizing RPA, companies can streamline processes, reduce costs and increase overall operational flexibility. The study delves into the theoretical underpinnings of RPA adoption, drawing on concepts such as economies of scale, process reengineering and information processing theory. In addition, the paper illuminates the transformative impact of RPA on business operations through field case studies, including the pioneering efforts of Sinochem International. By providing a comprehensive analysis of the pros and cons of RPA implementation, this paper provides valuable insights for Chinese companies seeking to optimize their business processes and embrace the era of intelligent automation.

Is Environmental Information Disclosure Driving Green Energy Development? Contextual evidence from Chinese city level data in the lens of sustainability

As an important supplement to mandatory environmental regulation, third-party participation in environmental regulation has become an important driving force for the comprehensive green transformation of economic and social development. Environmental information disclosure (EID) has become a widely used form of third-party participation ecological regulation, and the study of its impact on green energy development (GED) is receiving more and more academic attention. However, the research on this issue is still insufficient in academia. Based on resource-based theory and information processing theory, with IPE project of pollution information survey in China as an exogenous impact, this paper analyzes the causal impact of EID for GED using panel data of 285 Chinese cities from 2000 to 2020. The findings revealed that EID is a strong driver of GED in the long run, further amplified by the smart city pilot program. Besides, industrial structure transformation (IST), substantive green technological innovation (SGTI), non-substantive green technological innovation (NOSGTI), and green finance development (GFD) are important mediating mechanisms through which EID facilitates the GED. Concerning resource endowments of different regions and cities, the heterogeneity test asserted that the promotion effect of EID for GED is best in resource-based cities and old industrial parks. These results emphasize the importance of public participation in urban sustainable development and provide new ideas for promoting green growth.

Do Heuristic Cues Affect Misinformation Sharing? Evidence From a Meta-Analysis

Information overload in online environments leads individuals to be more likely to rely on heuristic cues to make decisions about (mis)information sharing. Drawing on dual-process models of information processing, namely the heuristic–systematic model and the elaboration likelihood model, we conducted a meta-analysis of 31 individual studies to examine the combined effects of five major heuristic cues on misinformation sharing. The results revealed that the affective heuristic of anxiety exerted the strongest impact on misinformation sharing ( r = .343), followed by three cognitive heuristics: the self-confirmation heuristic ( r = .249), endorsement heuristic ( r = .125), and fact-checking heuristic ( r = -.115)—but not the authority heuristic. The endorsement misinformation receives, consistency between misinformation and individuals’ prior attitudes or beliefs, anxiety people feel, and absence of fact-checking labels all make people more likely to share misinformation. Moderation analyses also highlight the importance of considering contextual and cultural factors in future research.

Cognitive Diversity and Initiative-Taking in Crisis: A Multisource Multilevel Dual-Path Model

Disruptive situations experienced in crises require significant initiative-taking among employees, particularly those in the service sector. Relying on the social information processing theory, this study expounds on how and when team cognitive diversity influences employee initiative-taking through work meaningfulness and perceived subgroup splits during a crisis. Data was collected from 232 hotel employees working within 58 work teams from two sources and in two waves. Results from a multilevel, dual-path parallel mediation data analysis did not support the hypothesized direct relationship between team cognitive diversity and personal initiative-taking or the indirect path through perceived subgroup split. This study’s analysis supported only the indirect path through work meaningfulness. Implications of the findings as well as avenues for further research and theory expansion are discussed.

Social cognition in adults with neurofibromatosis type 1.

Adult patients with the genetic disease neurofibromatosis type 1 (NF1) frequently report social difficulties. To date, however, only two studies have explored whether these difficulties are caused by social cognition deficits, and these yielded contradictory data. The aim of the present study was to exhaustively assess social cognition abilities (emotion, theory of mind, moral reasoning, and social information processing) in adults with NF1, compared with a control group, and to explore links between social cognition and disease characteristics (mode of inheritance, severity, and visibility). We administered a social cognition battery to 20 adults with NF1 (mean age = 26.5 years, SD = 7.4) and 20 healthy adults matched for sociodemographic variables. Patients scored significantly lower than controls on emotion, theory of mind, moral reasoning, and social information processing tasks. No effects of disease characteristics were found. These results appear to confirm that adults with NF1 have a social cognition weaknesses that could explain, at least in part, their social difficulties, although social abilities are not all impaired to the same extent. Regarding the impact of the disease characteristics, the patient sample seemed slightly insufficient for the power analyses performed. Thus, this exploratory study should form the basis of further research, with the objective of replicating these results with larger and more appropriately matched samples.

The Psychological Nature of Female Gait Attractiveness.

Walking, a basic physical movement of the human body, is a resource for observers in forming interpersonal impressions. We have previously investigated the expression and perception of the attractiveness of female gaits. In this paper, drawing on our previous research, additional analysis, and reviewing previous studies, we seek to deepen our understanding of the function of gait attractiveness. First, we review previous research on gait as nonverbal information. Then, we show that fashion models' gaits reflect sociocultural genderlessness, while nonmodels express reproductive-related biological attractiveness. Next, we discuss the functions of gait attractiveness based on statistical models that link gait parameters and attractiveness scores. Finally, we focus on observers' perception of attractiveness, constructing a model of the visual information processing with respect to gait attractiveness. Overall, our results suggest that there are not only biological but also sociocultural criteria for gait attractiveness, and men and women place greater importance on the former and latter criteria, respectively, when assessing female gait attractiveness. This paper forms a major step forward in neuroaesthetics to understand the beauty of the human body and the generation of biological motions.

The hidden gems in online reviews: unraveling how the expressions of affective needs impact review usefulness

ABSTRACT Recognizing the importance of affective fulfillment for contemporary customers, this study aims to investigate how the expressions of affective needs in online reviews impact review usefulness. Drawing on information processing theory, we proposed a framework based on big data analytics and econometrics to explore 469,202 online reviews. Empirical findings unveil that consumers’ expressions of affective needs involve Service, Room, Decoration, Enhancement facility, and Location; both the affective needs presence and intensity regarding the first four attributes positively influence review usefulness. The expressions of functional needs have bigger impacts than that of affective needs regarding Room but smaller than Service, Enhancement facility, and Decoration. Hotel type moderates this relationship: the influence of affective need expressions is amplified in luxury (vs. economy) hotels, indicating that their customers crave more disclosure for affective needs. This study contributes to research on review usefulness and hospitality market segmentation. Practical implications for multiple stakeholders are discussed.

How Algorithmic Management Influences Gig Workers' Job Crafting.

Algorithmic management, as a digital management tool in the gig economy, has become a hotspot of concern at the intersection of theory and practice. However, there is a lack of research on the mechanisms and boundary conditions through which algorithmic management affects gig workers' job crafting. Based on the social information processing theory, this study constructed a dual-mediation model of how algorithmic management influences gig workers' job crafting through gameful experience and perceived job autonomy. Data from 687 valid samples were collected through a two-stage survey and statistically analyzed using structural equation modeling (SEM). The results demonstrate that algorithmic management increases gig workers' promotion-focused job crafting behaviors (increasing job resources and challenging job demands) by stimulating their gameful experiences and increases gig workers' prevention-focused job crafting behaviors (decreasing hindering job demands) by inhibiting their perceived job autonomy. In addition, the higher-order personality trait core self-evaluation moderates the relationship between algorithmic management and gameful experience and perceived job autonomy, as well as the indirect effects of algorithmic management on job crafting through gameful experiences and perceived job autonomy. This study advances empirical research on algorithmic management in the field of the gig economy and human resource management practices. Crucially, it provides practical insights for optimizing algorithmic systems in platform companies, potentially enhancing their efficiency and economic benefits.

Natural Language Processing (NLP) Application For Classifying and Managing Tacit Knowledge in Revolutionizing AI-Driven Library

The rapid evolution of technology has transformed library systems, with Natural Language Processing (NLP) emerging as a pivotal tool for enhancing knowledge management. This study aims to examine how NLP can improve the classification and management of tacit knowledge within AI-driven libraries, addressing the challenge of handling large volumes of unstructured data. The objective is to explore how NLP can optimize the retrieval, organization, and access to tacit knowledge, thus enhancing decision-making processes in libraries. The research adopts a conceptual design, synthesizing existing literature and theoretical models, including Information Processing Theory and Constructivist Theory, to propose a framework that integrates NLP with traditional knowledge management practices. Methodologies include a thorough review of recent advancements in NLP technologies and their applications within knowledge management systems. The study’s findings demonstrate that NLP significantly improves the accuracy and efficiency of knowledge retrieval by automating the processing of natural language data. This allows better access to tacit knowledge, supporting more informed decision-making. The outcomes of the study are twofold: it enhances existing knowledge management frameworks theoretically, and it provides practical insights for libraries to leverage NLP for greater operational efficiency and improved user experience. The study also underscores the need for future research on the real-world application of NLP and its ethical implications, such as data privacy and algorithmic bias.

Shaken to the core: trust trajectories in the aftermaths of adverse cyber events

PurposeAdverse cyber events, like death and taxes, have become inevitable. They are an increasingly common feature of organisational life. Their aftermaths are a critical and under-examined context and dynamic space within which to examine trust. In this paper, we address this deficit.Design/methodology/approachDrawing on pertinent theory and reports of empirical studies, we outline the basis of two alternative subsequent trajectories, drawing out the relationships between trust, vulnerability and emotion, both positive and negative, in the aftermath of an adverse cyber event.FindingsWe combine stage theory and social information processing theories to delineate the dynamics of trust processes and their multilevel trajectories during adverse cyber event aftermaths. We consider two response trajectories to chart the way vulnerability arises at different levels within these social systems to create self-reinforcing trust and distrust spirals. These ripple out to impact multiple levels of the organisation by either amplifying or relieving vulnerability.Research limitations/implicationsThe way adverse cyber events aftermaths are managed has immediate and long-term consequences for organisational stakeholders. Actions impact resilience and the ability to preserve the social fabric of the organisations. Subsequent trajectories can be “negative” or “positive”. The “negative” trajectory is characterised by efforts to identify and punish the employee whose actions facilitated the adverse events, i.e. the “who”. Public scapegoating might follow thereby amplifying perceived vulnerability and reducing trust across the board. By contrast, the “positive” trajectory relieves perceived vulnerability by focusing on, and correcting, situational causatives. Here, the focus is on the “what” and “why” of the event.Practical implicationsWe raise the importance of responding in a constructive way to adverse cyber events.Originality/valueThe aftermaths of cyber attacks in organisations are a critical, neglected context. We explore the interplay between trust and vulnerability and its implications for management “best practice”.

The relationship between self-serving leadership and employees’ expediency: a moderated mediation model

PurposeDrawing upon social information processing theory, this study investigates the correlation between self-serving leadership and employee expediency. It also explores the mediating effect of self-interest motivation and the moderating effect of trait mindfulness.Design/methodology/approachA total of 147 part-time MBA students were enlisted to participate in a scenario experiment (Study 1), and 291 valid employee questionnaires were collected through a multiple-time point survey (Study 2). SPSS 23.0, MPLUS 8.0 and PROCESS programs were used to analyze the data and test the hypotheses.FindingsStudy 1 illustrated a positive correlation between self-serving leadership and employee expediency. It also identified self-interest motivation as a mediating factor in the correlation between self-serving leadership and expediency. Study 2 replicated the results obtained in Study 1 and expanded upon them by demonstrating that trait mindfulness moderates the association between self-serving leadership and self-interest motivation. Additionally, trait mindfulness moderates the indirect effect of self-serving leadership on expediency.Practical implicationsThis research argues that organizations should take steps to prevent self-serving leadership in order to reduce employee expediency. Furthermore, it is advisable to provide ethics training to employees who exhibit high trait mindfulness, as they show increased sensitivity to self-serving leadership and are more likely to engage in unethical behavior.Originality/valueThis study expands the existing research on the ethical outcomes of self-serving leadership and contributes to a deeper understanding of the negative aspects of trait mindfulness.

A discourse analysis of blind spots in Sinclair and Coulthard (1992) model of classroom discourse

This study investigates the blind spots in Sinclair and Coulthard’s revised classroom discourse (CD) structure of Teacher’s Initiation, Students’ Response and Teacher’s Feedback (IRF). The study suggests the Initiation (2), Response (2) and Feedback (2) (IIRRFF) pattern which may be suitable for today’s CD. This pattern permits equal contributions by classroom participants. The study adopts a quantitative method for data collection; a qualitative-descriptive research design and Information Processing Theory (IPT). Data were collected through a questionnaire distributed to English lecturers and students from three tertiary institutions in Nigeria. The results show a high percentage of participants who confirmed that Sinclair and Coulthard’s IRF model has blind spots; 86% of the respondents agreed that the model may lead to a limited understanding of what is taught; 80% agreed that dual initiations and feedback are rejected in this model, and 70% strongly agreed that the model should be expanded to accommodate other fields of language learning. The study recommends that CD should be categorized into two major types: the teacher’s utterances and the students’ utterances. The IIRRFF pattern that allows teachers and students to have equal slots in every CD should be implemented. This will encourage students’ full participation and interaction.

A mismatch between striatal cholinergic pauses and dopaminergic reward prediction errors

Striatal acetylcholine and dopamine critically regulate movement, motivation, and reward-related learning. Pauses in cholinergic interneuron (CIN) firing are thought to coincide with dopamine pulses encoding reward prediction errors (RPE) to jointly enable synaptic plasticity. Here, we examine the firing of identified CINs during reward-guided decision-making in freely moving rats and compare this firing to dopamine release. Relationships between CINs, dopamine, and behavior varied strongly by subregion. In the dorsal-lateral striatum, a Go! cue evoked burst-pause CIN spiking, followed by a brief dopamine pulse that was unrelated to RPE. In the dorsal-medial striatum, this cue evoked only a CIN pause, that was curtailed by a movement-selective rebound in firing. Finally, in the ventral striatum, a reward cue evoked RPE-coding increases in both dopamine and CIN firing, without a consistent pause. Our results demonstrate a spatial and temporal dissociation between CIN pauses and dopamine RPE signals and will inform future models of striatal information processing under both normal and pathological conditions.

From Health Risks to Environmental Actions: Research on the Pathway of Guiding Citizens to Participate in Pocket-Park Governance

The COVID-19 pandemic has heightened the demand for urban pocket parks near residential areas, posing new challenges for environmental governance. However, there is a lack of research on how to engage citizens in pocket-park governance to address both potential and unforeseen risks. This study combines social information processing theory with a norm activation model to develop a framework that identifies the shaping stages and influencing factors of citizens’ intentions to participate in pocket-park governance. Using partial least squares structural equation modeling, this research analyzed the relationships among external factors, attitudes, moral norms, and intentions to participate based on 719 responses from an online survey targeting Chinese citizens in November and December 2023. Results indicate that health risks and pocket-park environmental quality positively affect perceived usefulness. Attitudes and moral norms are indeed important factors mediating the positive effect of the external environment on the intention to participate. The pathway of guiding citizens to participate in pocket-park governance is clarified, which helps bolster the resilience of urban green spaces and improve the quality of life of residents after public crises.

Social Presence and Collaborative Creativity in Leaner Media

As people increasingly rely on virtual spaces for both personal and professional exchanges, the dynamics of how we connect, share ideas, and work together have evolved. Online interactions often engage in modality switching where communication starts in one modality, such as talking, then switches to another, such as texting. We examined how modality switching affected creativity and social presence when collaborating using computer mediated communication. Participants first met via an audio or text modality, then switched to the other modality. They then engaged in a collaborative story creation task. Using the Social Information Processing Theory for our theoretical framework, we found that switching from text to audio benefitted creativity. We found no differences between texted and audio communication in social presence, desire for future interaction, or creativity. Replicating others’ results, we did find differences in conversational balance: Texted conversations were more balanced than audio conversations. We newly discovered that modality switching affected balance. Audio conversations were more balanced when they followed text interactions rather than preceded them. Ultimately, more balanced conversations – regardless of modality – led to greater creativity in dyads.

The Psychological Mechanism of Moral Leadership Influencing Responsible Subordinate Behavior

As China’s anti-corruption campaign intensifies, several of those involved in various capacities in the public sector are demonstrating slackness, a lack of moral awareness, and a weakened sense of responsibility. This study analyzes the multiple mechanisms influencing the responsible behavior of public sector personnel by combining certain key factors, such as managers’ moral leadership style, psychological security, and the notion of interactive justice. Based on social information processing theory and social exchange theory, this study constructs a dual mediation model (psychological security and interactive justice) to explore the mediating psychological mechanism of moral leadership influencing employees’ responsible behavior. The results of a two-stage paired survey and structural equation modeling analysis of civil servants from 48 departments and eight units in Shandong and Henan provinces show that moral leadership positively impacts responsible behavior. Moreover, psychological security and interactive justice play a mediating role between them. Furthermore, this study elaborates upon the mechanism of moral leadership affecting the responsible behavior of employees, which is of great value for improving the efficiency of government management and guiding civil servants to behave more responsibly.

Using Gagene's Model for Enhancing EFL Students Innovative Thinking

Gagne's model of instructional design is based on the information processing model of the mental events that occur when adults are presented with various stimuli and focuses on the learning outcomes and how to arrange specific instructional events to achieve those outcomes. Innovative thinking is the ability to create something new. This study aims at, assessing the average level of the (4th) year secondary school students' achievement in thinking skill; finding out if there is any significant difference between the achievement of the experimental and control group in the posttest; finding out if there is any significant difference between the experimental group's achievement at the recognition and the production levels in the posttest; finding out if there is any significant difference among the five variables, vocabulary, grammar, clarity, accuracy and precision. In order to fulfill the aims of the study, the following null hypotheses have been posted; there is a statistically significant difference between the average level of the students' achievement and theoretical level of achievement in the posttest; there is a statistically significant difference between the mean scores of the control group, who are taught according to the conventional method and the mean scores of the experimental group who are taught according to the conventional method and the mean scores of the experimental group who are taught by using of Gagne’s Model; there is a statistically significant difference between the mean scores of the experimental group's achievement at the recognition and the production levels in the posttest; there is a statistically significant difference between students' mean scores of the five variables, vocabulary, grammar, clarity, accuracy and precision in the posttest.

Asymmetric Network Combining CNN and Transformer for Building Extraction from Remote Sensing Images.

The accurate extraction of buildings from remote sensing images is crucial in fields such as 3D urban planning, disaster detection, and military reconnaissance. In recent years, models based on Transformer have performed well in global information processing and contextual relationship modeling, but suffer from high computational costs and insufficient ability to capture local information. In contrast, convolutional neural networks (CNNs) are very effective in extracting local features, but have a limited ability to process global information. In this paper, an asymmetric network (CTANet), which combines the advantages of CNN and Transformer, is proposed to achieve efficient extraction of buildings. Specifically, CTANet employs ConvNeXt as an encoder to extract features and combines it with an efficient bilateral hybrid attention transformer (BHAFormer) which is designed as a decoder. The BHAFormer establishes global dependencies from both texture edge features and background information perspectives to extract buildings more accurately while maintaining a low computational cost. Additionally, the multiscale mixed attention mechanism module (MSM-AMM) is introduced to learn the multiscale semantic information and channel representations of the encoder features to reduce noise interference and compensate for the loss of information in the downsampling process. Experimental results show that the proposed model achieves the best F1-score (86.7%, 95.74%, and 90.52%) and IoU (76.52%, 91.84%, and 82.68%) compared to other state-of-the-art methods on the Massachusetts building dataset, the WHU building dataset, and the Inria aerial image labeling dataset.