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Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues.

Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets.

The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns.

The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.

This study investigates taxpayers’ perception to the Jordanian tax system efficiency according to the perspective of Keynes. Its main purpose is to determine whether taxpayers perceive the Jordanian tax system as efficient, enough to influence taxpayers’ patterns of behavior, or inefficient and just used to collect revenue by the government. A self‐administered questionnaire is used to collect the primary data of the study, in order to measure the economical and socio‐cultural efficiency of the system. A convenience sample consisting of 175 respondents was selected to survey how taxpayers perceive the Jordanian tax system efficiency. The t‐test is used as a decision criterion for the acceptance or rejection of the hypotheses. Correlation analysis is also used to support the findings of the study. The study finds that taxpayers perceive the Jordanian tax system as efficient, and they perceive that the tax system is intentionally used to influence their behavior.

Past research on the motivational processes underpinning knowledge sharing has assumed that the sharing processes are similar for all individuals. Yet, sharing is a fundamental affiliative behavior, and the sharing processes can differ between people. This study aims to propose and test a model of the moderating influence that employee attachment patterns have on the theory of reasoned action (TRA)-defined knowledge sharing processes.

The authors administered a questionnaire to 1,103 employees from a range of industries who participated in an online Qualtrics survey. Advanced forms for structural equation modeling and latent profile analysis were used to assess the proposed model.

The results revealed that participants in the study exhibited the latent profiles corresponding to secure, dismissive, preoccupied and fearful patterns. The preoccupied cohort had the lowest knowledge sharing behavior, yet the strongest links within the sharing process. Secure, dismissive and fearful had similar sharing levels, but the strength of the TRA-defined processes differed. These findings underscore equifinality: although sharing may be approximately equal across different attachment patterns, the fundamental processes underpinning sharing differ.

The authors used self-report data, given that sharing attitudes, norms and intentions may not be overly amenable to ratings even from well-acquainted others. Further, the use of advanced analytical methods helps to minimize common method concerns. Additionally, causal mechanisms underscoring the TRA have been demonstrated (Ajzen and Fishbein, 2005), allowing us to explore the moderating role of attachment patterns.

This study speaks to the importance of considering employees’ attachment patterns, and developing comprehensive intra-organizational norms, policies and systems that support and encourage knowledge sharing from employees with a variety of attachment patterns.

This study uniquely contributes to knowledge sharing literatures by incorporating attachment patterns as moderators within the TRA-defined sharing processes. The authors provide important insights on the role of individuals’ attachment patterns have for knowledge sharing behaviors, but also highlight how structure of knowledge sharing differed across subgroups of employees, determined based on their dispositional attachment pattern.

The purpose of this paper is to analyze how pattern recognition can contribute to the behavioral options of a goal‐oriented system.

A functional approach is used to develop the necessary cybernetic structures of a pattern recognition unit that can store observations as new standards for pattern matching by itself and can later apply them to recognize patterns in incoming sensor data.

Combining such a structure for pattern recognition with a feedback system shows that the resulting system can only deal with known patterns. To deal with novel patterns this structure has to be added to an adaptive system that can develop system‐specific behavior. Such a system has to able to initiate a trial and error process to test new behavior towards new patterns and to evaluate its effect on the highest, existential goal‐values of the system.

A system with a pattern recognition unit that can set new standards for pattern matching by itself is identified as the point of departure where not‐programmable and unpredictable individual behavior starts. Dealing with newly‐recognized pattern requires individual behavioral solutions and a system‐specific evaluation of the achieved results in relation to the highest goal‐values of the system. Here internal “emotional” criteria to select behavior emerge as a cybernetic necessity.

The paper is the third in a series of three on a cybernetic theory distinguishing system capable of pre‐programmed adaptation, system‐specific adaptation and learning. It determines the cybernetic starting point of individual psychology.

In recent years, the increasing complexity of the hyper-connected world demands new approaches for social network analysis. The main challenges are to find new computational methods that allow the representation, characterization and analysis of these social networks. Nowadays, formal concept analysis (FCA) is considered an alternative to identifying conceptual structures in a social network. In this FCA-based work, this paper aims to show the potential of building computational models based on implications to represent and analyze two-mode networks.

This study proposes an approach to find three important substructures in social networks such as conservative access patterns, minimum behavior patterns and canonical access patterns. The present study approach considered as a case study a database containing the access logs of a cable internet service provider.

The result allows us to uncover access patterns, conservative access patterns and minimum access behavior patterns. Furthermore, through the use of implications sets, the relationships between event-type elements (websites) in two-mode networks are analyzed. This paper discusses, in a generic form, the adopted procedures that can be extended to other social networks.

A new approach is proposed for the identification of conservative behavior in two-mode networks. The proper implications needed to handle minimum behavior pattern in two-mode networks is also proposed to be analyzed. The one-item conclusion implications are easy to understand and can be more relevant to anyone looking for one particular website access pattern. Finally, a method for a canonical behavior representation in two-mode networks using a canonical set of implications (steam base), which present a minimal set of implications without loss of information, is proposed.

COVID-19 vaccinations face a backdrop of widespread mistrust in their safety and effectiveness, specifically via social media platforms which constitute major barriers for the public health sector to manage COVID-19 (and future) pandemics. This study provides a more nuanced understanding of the public's engagement behavior toward COVID-19 vaccinations.

Using Netnography, this study explores the public's interactions with vaccine communications by the WHO via Facebook. From WHO posts about the COVID-19 vaccination 23,726 public comments on Facebook were extracted and analyzed.

Building on crisis communication, health and engagement literature, this paper identifies and conceptualizes seven patterns of engagement behavior toward the COVID-19 vaccination and develops the first framework of relationships between these patterns and the extant vaccine attitudes: vaccine acceptance, hesitancy and refusal.

This paper helps policymakers identify and adapt interventions that increase vaccine confidence and tailor public health services communications accordingly.

This research offers the first typology of patterns of engagement behavior toward COVID-19 vaccinations and develops a framework of relationships between these patterns and the existing understanding in health literature. Finally, the study provides data-driven communication recommendations to public health service organizations.

This study examines Pateman's “spillover thesis” that democratic participation in the workplace will “spill over” into political participation. It applies a latent class analysis (LCA) to identify patterns of political behavior and uses workplace participation and political efficacy as predicting variables of political behavior patterns.

This study analyzed the International Social Survey Programme (ISSP) in 2014 General Social Survey (GSS) data. This study applied a LCA to identify distinct patterns in people's political behaviors and did a multinomial regression analysis to predict the patterns with workplace participation and political efficacy.

The study found partial support for the spillover thesis. Among three distinct political behavior patterns, two active patterns were associated with political efficacy. However, the mediation from workplace participation to political participation through political efficacy was not supported. Respondents involved in workplace units that collectively make work-related decisions were more likely to be active in political behaviors, but only one set of political activities. Higher political efficacy was found to lead to more active overall political participation of both patterns.

Unlike the previous studies of democratic spillover, which treated political behaviors either as independent types of behaviors or as a summative index of such binary coded variables, this study addressed such shortcomings of the previous studies by providing a more complex picture of political behavior patterns and their relationship with workplace participation. Future research can build on this unique methodological endeavor to explore a holistic picture of how workplace practices can influence politics and democracy through individual workers.

The aim of this paper is to show that online learning behaviors are dictated by both personal characteristics and regional differences.

Data were collected from 16,133 users in 25 regions of Taiwan. The paper examined usage behaviors by looking at 11 items of categorical variables about online learning. This study implemented a multi‐level latent class model to investigate online learning behavior patterns that exhibit regional differences.

The results showed that online learning patterns do exhibit regional differences, as the regional segments are dictated by the individual segments of different use patterns. For instance, the urban area segment comprised a higher proportion of members who are good at using the internet. The rural area segment made up a higher proportion of members who occasionally use the internet. Interestingly, rural users went online more often than urban users when in search of e‐learning or entertainment. On the other hand, the individual segments are dictated by users' personal characteristics. For instance, younger people are good at employing online learning and entertainment services. Moreover, those who use many types of online applications pay less respect to intellectual property rights than those who only use a few types of applications.

By using a massive amount of survey data to show regional differences in online learning behavior patterns, the findings herein will help internet service providers form an applicable guideline for developing service strategies of higher service satisfaction between products and users' needs.

The purpose of this article is to investigate urban and rural differences for online activities and e‐payment behavior patterns.

This study applied the MLCA model to investigate Internet usage patterns from 11 online applications among 10,909 Taiwan residents in 25 different regions.

The results showed that online behavior patterns exhibited regional differences, as the regional segments affected the individual segments of different use patterns. For instance, the urban area comprised a higher proportion of members who were accustomed to internet applications and skilled in online shopping by using a credit card. The rural area made up a higher proportion of members who only occasionally used online services. Moreover, rural region residents used other payment methods (excluding credit cards) more often than urban region residents. As expected, users’ personal characteristics also dictated the online behavior pattern. For instance, people with higher‐level income spent relatively more money for online shopping and often used various internet applications than others.

The findings herein should help Internet service providers form an applicable guideline for developing service strategies of higher service satisfaction regarding products and users’ needs.

This study implemented a multilevel latent class model to investigate online behavior patterns that exhibited urban and rural differences, with the goal of providing service providers an understanding and mastery of their target users.

Properly conceived, conducted and interpreted, motivation research can be an extremely powerful management tool, designed to help the manufacturer or advertiser to sell more goods. Its aim is to expose the market situation, explain it and suggest courses of action which will lead to desired changes. It is a way of looking at a problem rather than a collection of specialist techniques and is strictly practical. Hence it can be used alongside other market research tools for the solution of marketing problems and can be applied to a wide range of business activities. Much of its development has been in the advertising field but it can also help in the formulation of production policy, solving packaging problems and marketing operations. It is examined here in all these contexts. The idea of motivation research, the reasons for its use and the techniques by which to apply it are discussed, as well as the pitfalls that are likely to occur. New and imaginary case studies are used throughout to illustrate points. A review of the subject literature is included.