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In order to deepen understanding of team processes in dynamic organizational contexts, we suggest that analyses employing techniques to identify and analyze team member interaction patterns and trajectories are necessary. After presenting a brief review of interaction data coding and reliability requirements, we first review examples of two approaches used in the identification and analysis of interaction patterns in teams: lag sequential analysis and T-pattern analysis. We then describe and discuss three statistical techniques used to analyze team interaction trajectories: random coefficient modeling, latent growth modeling, and discontinuous growth analysis. We close by suggesting several ways in which these techniques could be applied to data analysis in order to expand our knowledge of team interaction, processes, and outcomes in complex and dynamic settings.

On many occasions, organizational science research has been referred to as fragmented and disjointed, resulting in a literature that is, in the opinion of many, difficult to navigate and comprehend. One potential explanation is that scholars have failed to comprehend that organizations are complex and intricate systems. In order to move us past this morass, we recommend that researchers extend beyond traditional rational, mechanistic, and variable-centered approaches to research and integrate a more advantageous pattern-oriented approach within their research program. Pattern-oriented methods approximate real-life phenomena by adopting a holistic, integrative approach to research wherein individual- and organizational-systems are viewed as non-decomposable organized wholes. We argue that the pattern-oriented approach has the potential to overcome a number of breakdowns faced by alternate approaches, while offering a novel and more representative lens from which to view organizational- and HRM-related issues. The proposed incorporation of the pattern-oriented approach is framed within a review and evaluation of current approaches to organizational research and is supplemented with a discussion of methodological and theoretical implications as well as potential applications of the pattern-oriented approach.

Dietary patterns have been used to explore the association between dietary factors and risk of pancreatic cancer (PC) and renal cancer (RC); however, the association remains unclear. The purpose of this paper is to comprehensively review these associations.

Pertinent studies published prior to March 2016 were systematically searched and retrieved through PubMed and Scopus databases. Adjusted risk estimates were derived by comparing the highest with the lowest categories of dietary pattern scores and were combined by using the fixed-effects model when no substantial heterogeneity was observed; otherwise, the random-effects model was used.

A total of nine studies, five for PC (including 2,059 cases and 41,774 participants/controls) and four for RC (with 1,327 cases and 53,007 participants/controls), were included in this meta-analysis. A decreased risk of PC was shown for the highest compared with the lowest categories of the healthy dietary pattern (OR = 0.72, 95 per cent CI = 0.51-0.94, random effects (p-value for heterogeneity = 0.004)), whereas no significant association with Western dietary was observed (OR = 1.16, 95 per cent CI = 0.87-1.44, fixed effects). In the overall analysis, a significant association was found between the healthy dietary pattern and reduced risk of RC (OR = 0.59, 95 per cent CI = 0.48-0.71, fixed effects (p-value for heterogeneity = 0.459)), whereas the Western pattern was positively associated with risk of RC (OR = 1.42, 95 per cent CI = 1.14-1.69, fixed effects). For both cancers, the reduced risk associated with the healthy pattern was restricted to case-control, but not cohort, studies. Furthermore, drinking pattern was significantly related to reduced risk of RC (OR = 0.68, 95 per cent CI = 0.42-0.94).

To the authors’ knowledge, the present study is the first English document to summarize systematically the findings from observational studies in response to this question whether a posteriori dietary patterns are associated with susceptibility to the risk of renal and ovarian cancers.

The article aims to present a holistic approach to analysis of patterns on complex online profiles, demonstrated on profiles of European scientists.

An existing analytical framework was developed to incorporate a holistic understanding of online profiles. The framework was applied to a sample of 188 online profiles belonging to 48 European scientists. The profile data were studied on three levels (content‐unit level, profile‐instance level, and profile‐network level), using methods of the qualitative comparative analysis to derive profiling patterns.

The approach developed in this work generated profiling patterns for European scientists. The patterns exist on all three levels, forming a hierarchy. This pattern structure shows the variety of ways in which scientists can use the internet for self‐presentation.

The study was based on a holistic understanding of online self‐presentation, acknowledging that personal presentation can be spread across different platforms. The study presented shows how this understanding can be used when analysing online profiling behaviour. The profiling patterns of European scientists identified in this study supplement existing typologies. The study serves as a foundation to structure further research as well as to inform practitioners.

Research on antecedents of trust has, so far, yielded results that do not easily stand up to confrontation with the widely‐held assumption of bounded rationality. By employing complex constructs as indicators of antecedents, it is implied that actors, in pondering on trust in managers, can deal with many complex cues, instead of a few single ones, as bounded rationality suggests. This study proposes a different approach, by searching for a parsimonious set of managerial behaviours that serve as cues for subordinates regarding trust in managers. Interview and survey data were combined in this search. Regression analysis and a Boolean pattern analysis were used to arrive at a parsimonious model with high explanatory power.

Researchers in the past have used Fourier transformation method to determine the in‐plane displacement components from moiré fringes generated by a pair of overlapping circular gratings. In this approach it is necessary to assume that the transmittance is sinusoidal. The purpose of this paper is to propose a graphical method for determining the 2D displacement components from the moiré patterns more easily instead of the complex Fourier transformation method.

The moiré patterns were spatially transformed from Cartesian‐to‐polar coordinate system. The morphological grayscale dilation operation was used to eliminate the residual gratings in the transformed pattern while preserving the moiré fringes. The center line of the moiré fringe was fitted with a sine curve and the in‐plane displacement values were determined directly from the peak‐to‐valley height and the position of the peak in the fitted curve.

Experimental results showed that the proposed moiré pattern analysis method is able to give in‐plane displacement accuracies of 0.002 mm in the x‐direction and 0.01 in the y‐direction without the need for complex computation.

Resolution of the proposed method is limited only by the resolution of the imaging system.

The proposed graphical method for determining 2D displacement components from the moiré patterns can be applied to low‐frequency circular gratings whose transmittance is not sinusoidal.

The graphical analysis method is novel and allows the displacements components to be determined more easily.

The purpose of this paper is to investigate available forensic data on the Bitcoin blockchain to identify typical transaction patterns of ransomware attacks. Specifically, the authors explore how distinct these patterns are and their potential value for intelligence exploitation in support of countering ransomware attacks.

The authors created an analytic framework – the Ransomware–Bitcoin Intelligence–Forensic Continuum framework – to search for transaction patterns in the blockchain records from actual ransomware attacks. Data of a number of different ransomware Bitcoin addresses was extracted to populate the framework, via the WalletExplorer.com programming interface. This data was then assembled in a representation of the target network for pattern analysis on the input (cash-in) and output (cash-out) side of the ransomware seed addresses. Different graph algorithms were applied to these networks. The results were compared to a “control” network derived from a Bitcoin charity.

The findings show discernible patterns in the network relating to the input and output side of the ransomware graphs. However, these patterns are not easily distinguishable from those associated with the charity Bitcoin address on the input side. Nonetheless, the collection profile over time is more volatile than with the charity Bitcoin address. On the other hand, ransomware output patterns differ from those associated charity addresses, as the attacker cash-out tactics are quite different from the way charities mobilise their donations. We further argue that an application of graph machine learning provides a basis for future analysis and data refinement possibilities.

Limitations are evident in the sample size of data taken on ransomware campaigns and the “control” subject. Further analysis of additional ransomware campaigns and “control” subjects over time would help refine and validate the preliminary observations in this paper. Future research will also benefit from the application of more powerful computing resources and analytics platforms that scale with the amount of data being collected.

This research contributes to the maturity of the field by analysing ransomware-Bitcoin behaviour using the Ransomware–Bitcoin Intelligence–Forensic Continuum. By combining several different techniques to discerning patterns of ransomware activity on the Bitcoin network, it provides insight into whether a ransomware attack is occurring and could be used to trigger alerts to seek additional evidence of attack, or could corroborate other information in the system.

This study aims to propose a learning pattern analysis method which can improve a predictive model’s performance, as well as discover hidden insights into micro-level learning pattern. Analyzing student’s learning patterns can help instructors understand how their course design or activities shape learning behaviors; depict students’ beliefs about learning and their motivation; and predict learning performance by analyzing individual students’ learning patterns. Although time-series analysis is one of the most feasible predictive methods for learning pattern analysis, literature-indicated current approaches cannot provide holistic insights about learning patterns for personalized intervention. This study identified at-risk students by micro-level learning pattern analysis and detected pattern types, especially at-risk patterns that existed in the case study. The connections among students’ learning patterns, corresponding self-regulated learning (SRL) strategies and learning performance were finally revealed.

The method used long short-term memory (LSTM)-encoder to process micro-level behavioral patterns for feature extraction and compression, thus the students’ behavior pattern information were saved into encoded series. The encoded time-series data were then used for pattern analysis and performance prediction. Time series clustering were performed to interpret the unique strength of proposed method.

Successful students showed consistent participation levels and balanced behavioral frequency distributions. The successful students also adjusted learning behaviors to meet with course requirements accordingly. The three at-risk patten types showed the low-engagement (R1) the low-interaction (R2) and the non-persistent characteristics (R3). Successful students showed more complete SRL strategies than failed students. Political Science had higher at-risk chances in all three at-risk types. Computer Science, Earth Science and Economics showed higher chances of having R3 students.

The study identified multiple learning patterns which can lead to the at-risk situation. However, more studies are needed to validate whether the same at-risk types can be found in other educational settings. In addition, this case study found the distributions of at-risk types were vary in different subjects. The relationship between subjects and at-risk types is worth further investigation.

This study found the proposed method can effectively extract micro-level behavioral information to generate better prediction outcomes and depict student’s SRL learning strategies in online learning. The authors confirm that the research in their work is original, and that all the data given in the paper are real and authentic. The study has not been submitted to peer review and not has been accepted for publishing in another journal.

Since a firm’s management performance can be evaluated in terms of financial ratios, efficient management using financial factors is proposed as the key element for upgrading a firm’s productivity. Investigates productivity in terms of certain financial factors of large‐scale manufacturing firms in Taiwan. First determines several influential financial factors using factor analysis. Based on these factors, employs fuzzy clustering approaches to categorize the manufacturing firms into several patterns with distinct characteristics of financial factors. Using the characteristics of productivity and financial factors for each pattern, makes two kinds of analysis, and proposes some suggestions to improve the firms’ productivity.