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Despite corporate communications having an immense impact on corporate success, there is a lack of dedicated techniques for their management and visualization. A potential strategy is to apply business process management (BPM) approach with business process model and notation (BPMN) modeling techniques.

The goal of this study was to gain empirical insights into the cognitive effectiveness of BPMN-based corporate communications modeling. To this end, experimental research was performed in which subjects tested two modeling notations – standardized BPMN conversation diagrams and a BPMN extension with corporate communications-specific concepts.

Standard conversation diagrams were demonstrated to be more time-efficient for designing and interpreting diagrams. However, the subjects made significantly fewer mistakes when interpreting the diagrams modeled in the BPMN extension. Subjects also evolved positive perceptions toward the proposed extension.

BPMN-based corporate communications modeling may be applied to organizations to depict how formal communications are or should be performed consistently, effectively and transparently by following and integrating with BPM approaches and modeling techniques.

The paper provides empirical insights into the cognitive effectiveness of corporate communications modeling based on BPMN and positions the corresponding models into typical process architecture.

In the development processes of a product’s market life cycle, there are three phases of an enterprise’s innovation: new product development, production processes, and product management. In this article, the analyses of benefit and costs, value, and profit to companies are discussed in different stages. New logistics features that appear in an enterprise’s supply chain based on innovative modeling are discussed. Then a logistics model and its technical system based on the classified logistics center are established, which are appropriate for innovative modeling within an agile supply chain. Using the basic theory and techniques of ‘extenics’, the formal conception of innovative modeling-based manufacture in logistics is presented, and the matter-element models are established. Finally, a case study demonstrates the results.

The paper explored the benefits as well as the concerns of vocabulary learning with clay modeling in terms of practical and pedagogical implications for creating positive learning experiences.

A mixed-methods design was conducted to examine the effectiveness of vocabulary learning with clay modeling practices in lower socioeconomic status schools.

Although test results showed no statistically significant differences between the groups, the clay modeling group did improve vocabulary acquisition similar to the sentence writing group. The students were actively engaged with hands-on activities using the clay and also demonstrated positive emotional, behavioral and physical experiences.

The addition of the clay modeling provided an opportunity for kinesthetic learning but created a high extraneous cognitive load with the challenges incurred through the use of clay.

The challenges can be reduced by 1) adopting appropriate instructional strategies to design and implement effective clay modeling activities for students and teachers, 2) providing training or professional workshop development for teachers and 3) ongoing practical support and assistance for students.

Exploring the use of kinesthetic instructional practice at the high school level may prove beneficial since clay modeling is frequently used effectively at lower grade levels.

The current study explores the added value of clay modeling for high school students’ biology vocabulary learning in a lower socioeconomic status school from practical and pedagogical perspectives.

This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM.

This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models.

This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables.

Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously.

To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables.

This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.

Partial least squares (PLS) path modeling is a variance-based structural equation modeling (SEM) technique that is widely applied in business and social sciences. Its ability to model composites and factors makes it a formidable statistical tool for new technology research. Recent reviews, discussions, and developments have led to substantial changes in the understanding and use of PLS. The paper aims to discuss these issues.

This paper aggregates new insights and offers a fresh look at PLS path modeling. It presents new developments, such as consistent PLS, confirmatory composite analysis, and the heterotrait-monotrait ratio of correlations.

PLS path modeling is the method of choice if a SEM contains both factors and composites. Novel tests of exact fit make a confirmatory use of PLS path modeling possible.

This paper provides updated guidelines of how to use PLS and how to report and interpret its results.

The purpose of this article is to provide an overview of agent-based modeling as an alternative method for public administration research. It is focused on encouraging public administration scholars to come to better understanding of the method.

This article performed a comprehensive review of methodological issues relative to agent-based modeling.

After reviewing the current research themes in public administration and the methodological nature of agent-based modeling, we found that agent-based modeling can help researchers to advance theories by means of sophisticated thought experiment which is not possible by formal modeling and verbal reasoning. We also pointed out that agent-based modeling does not substitute empirical research, but can add much value through being part of a mixed-method and multidisciplinary research.

We suggested that interested researchers may need to take a strategic approach in developing and describing a pertinent model and reporting its results.

Agent-based modeling has rarely been used in public administration research. The article provides an introductory overview for researchers not familiar with ABM and suggests to the academic community future venues that would unfold from agent-based modeling.

Technology-facilitated abuse, so-called “tech abuse,” through phones, trackers, and other emerging innovations, has a substantial impact on the nature of intimate partner violence (IPV). The current chapter examines the risks and harms posed to IPV victims/survivors from the burgeoning Internet of Things (IoT) environment. IoT systems are understood as “smart” devices such as conventional household appliances that are connected to the internet. Interdependencies between different products together with the devices' enhanced functionalities offer opportunities for coercion and control. Across the chapter, we use the example of IoT to showcase how and why tech abuse is a socio-technological issue and requires not only human-centered (i.e., societal) but also cybersecurity (i.e., technical) responses. We apply the method of “threat modeling,” which is a process used to investigate potential cybersecurity attacks, to shift the conventional technical focus from the risks to systems toward risks to people. Through the analysis of a smart lock, we highlight insufficiently designed IoT privacy and security features and uncover how seemingly neutral design decisions can constrain, shape, and facilitate coercive and controlling behaviors.

This paper explores the Adoption of Technological Innovation (ATI) in the healthcare industry. It investigates how the literature has evolved, and what are the emerging innovation dimensions in the healthcare industry adoption studies.

We followed a mixed-method approach combining bibliometric methods and topic modeling, with 57 papers being deeply analyzed.

Our results identify three latent topics. The first one is related to the digitalization in healthcare with a specific focus on the COVID-19 pandemic. The second one groups up the word combinations dealing with the research models and their constructs. The third one refers to the healthcare systems/professionals and their resistance to ATI.

The study’s sample selection focused on scientific journals included in the Academic Journal Guide and in the FT Research Rank. However, the paper identifies trends that offer managerial insights for stakeholders in the healthcare industry.

ATI has the potential to revolutionize the health service delivery system and to decentralize services traditionally provided in hospitals or medical centers. All this would contribute to a reduction in waiting lists and the provision of proximity services.

The originality of the paper lies in the combination of two methods: bibliometric analysis and topic modeling. This approach allowed us to understand the ATI evolutions in the healthcare industry.

A significant number of studies have been conducted to analyze and understand the relationship between gas emissions and global temperature using conventional statistical approaches. However, these techniques follow assumptions of probabilistic modeling, where results can be associated with large errors. Furthermore, such traditional techniques cannot be applied to imprecise data. The purpose of this paper is to avoid strict assumptions when studying the complex relationships between variables by using the three innovative, up-to-date, statistical modeling tools: adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANNs) and fuzzy time series models.

These three approaches enabled us to effectively represent the relationship between global carbon dioxide (CO₂) emissions from the energy sector (oil, gas and coal) and the average global temperature increase. Temperature was used in this study (1900-2012). Investigations were conducted into the predictive power and performance of different fuzzy techniques against conventional methods and among the fuzzy techniques themselves.

A performance comparison of the ANFIS model against conventional techniques showed that the root means square error (RMSE) of ANFIS and conventional techniques were found to be 0.1157 and 0.1915, respectively. On the other hand, the correlation coefficients of ANN and the conventional technique were computed to be 0.93 and 0.69, respectively. Furthermore, the fuzzy-based time series analysis of CO₂ emissions and average global temperature using three fuzzy time series modeling techniques (Singh, Abbasov–Mamedova and NFTS) showed that the RMSE of fuzzy and conventional time series models were 110.51 and 1237.10, respectively.

The paper provides more awareness about fuzzy techniques application in CO₂ emissions studies.

These techniques can be extended to other models to assess the impact of CO₂ emission from other sectors.