Building Acquisition Management Capability to Improve Firm Performance in Acquisitions

In the past two decades, the number of cross-border mergers and acquisitions in ASEAN has progressively expanded as the region has become a desired economic market for trade and investment. Therefore, this study aimed to identify the factors contributing to the success of acquisitions by corporations. It investigates the role of acquisition management capability with strategic integration and acquisition. The non-probability sampling strategy was used to collect information from 51 firms. With a five-point Likert scale, a systematic questionnaire was designed to test the latent variables by employing confirmatory factor analysis. The quantitative method of Structural Equation Modeling was used in the analysis. The results show that the structural model had a Goodness of Fit Index value that indicates all three latent variables and independent variables were valid. The findings indicate that acquisition management capability have a central role in advancing the overall integration of the acquiring firm in the ASEAN context.

Multimodal Spatio-Temporal Prediction with Stochastic Adversarial Networks

Spatio-temporal (ST) data is a collection of multiple time series data with different spatial locations and is inherently stochastic and unpredictable. An accurate prediction over such data is an important building block for several urban applications, such as taxi demand prediction, traffic flow prediction, and so on. Existing deep learning based approaches assume that outcome is deterministic and there is only one plausible future; therefore, cannot capture the multimodal nature of future contents and dynamics. In addition, existing approaches learn spatial and temporal data separately as they assume weak correlation between them. To handle these issues, in this article, we propose a stochastic spatio-temporal generative model (named D-GAN) which adopts Generative Adversarial Networks (GANs)-based structure for more accurate ST prediction in multiple time steps. D-GAN consists of two components: (1) spatio-temporal correlation network which models spatio-temporal joint distribution of pixels and supports a stochastic sampling of latent variables for multiple plausible futures; (2) a stochastic adversarial network to jointly learn generation and variational inference of data through implicit distribution modeling. D-GAN also supports fusion of external factors through explicit objective to improve the model learning. Extensive experiments performed on two real-world datasets show that D-GAN achieves significant improvements and outperforms baseline models.

Factorisation-Based Image Labelling

Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging. Manual annotation is time consuming and expensive, so having a fully automated and general purpose brain segmentation algorithm is highly desirable. To this end, we propose a patched-based labell propagation approach based on a generative model with latent variables. Once trained, our Factorisation-based Image Labelling (FIL) model is able to label target images with a variety of image contrasts. We compare the effectiveness of our proposed model against the state-of-the-art using data from the MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labelling. As our approach is intended to be general purpose, we also assess how well it can handle domain shift by labelling images of the same subjects acquired with different MR contrasts.

Inversion of low to medium frequency velocities and densities from AVO data using invertible neural networks

AVO (amplitude variation with offset) inversion and neural networks are widely used to invert elastic parameters. With more constraints from well log data, neural network-based inversion may estimate elastic parameters with greater precision and resolution than traditional AVO inversion, however, neural network approaches necessitate a massive number of reliable training samples. Furthermore, because the lack of low-frequency information in seismic gathers leads to multiple solutions of the inverse problem, both inversions rely heavily on proper low-frequency initial models. To mitigate the dependence of inversions on accurate training samples and initial models, we propose solving inverse problems with the recently developed invertible neural networks (INNs). Unlike conventional neural networks, which address the ambiguous inverse issues directly, INNs learn definite forward modeling and use additional latent variables to increase the uniqueness of solutions. Motivated by the newly developed neural networks, we propose an INN-based AVO inversion method, which can reliably invert low to medium frequency velocities and densities with randomly generated easy-to-access datasets rather than trustworthy training samples or well-prepared initial models. Tests on synthetic and field data show that our method is feasible, anti-noise capable, and practicable.

Social Psychology and the Willingness of Different Citizens to Participate in a Car Lottery

To investigate citizens’ participation behavior in the lottery under the influence of the license plate lottery policy (LPLP) and to guide them to participate in the lottery rationally, this paper, based on social psychology and combined with the theory of planned behavior, divides citizens into citizens with cars in their households and citizens without cars in their households. This study then separately constructs structural equation models, sets perceived car necessity (PCN), perceived behavioral control (PBC), attitude toward car ownership (ATT), and subjective norms (SN), respectively. These four psychological latent variables were used to analyze the participation behavior of different categories of citizens in the car lottery from the perspective of psychological factors. Our empirical study found that there are significant differences in age and the number of people living together. The mechanism of their intention to participate in the car lottery and the psychological factors are different. The psychological factors affecting the intention of people with a car and people without a car to participate in the car lottery are SN > ATT > PCN > PBC and ATT > SN > PBC, respectively. Our research results can help to identify the internal factors and mechanisms that influence citizens’ intention to participate in the car lottery and help government administrators to optimize the LPLP.

ANALISIS TECHNOLOGY ACCEPTANCE MODEL PADA APLIKASI PLATFORM SHOPEE DENGAN PENDEKATAN PARTIAL LEAST SQUARE (STUDI KASUS PADA MAHASISWA UNIVERSITAS DIPONEGORO)

E-commerce refers to business transactions using digital networks such as the internet. Based on the rank on the Appstore and Playstore, Shopee places the first rank. In 2019, Shopee had 56 million visitors. Meanwhile, in the same year, it had 3,225 workers. The imbalance between the number of Shopee visitors and Shopee employees allows users to be disappointed with Shopee's services, but on the other hand, there are also many users who are happy with its services. With both positive and negative responses to the services provided by Shopee, this study analyzes the factors affecting the acceptance of Shopee Apps on students of Universitas Diponegoro Semarang. The analysis was based on the Technology Acceptance Model (TAM). It used the Structural Equation Modeling with the Partial Least Square (SEM-PLS) approach. The study used primary data obtained by distributing questionnaires to students of Universitas Diponegoro. The result showed 28 valid indicators, 5 deal inner models, and 8 significant pathways. All the causality between latent variables contained in the Technology Acceptance Model (TAM) have a positive and significant effect, it's just that the results of integrating trust variables on TAM, namely the latent variable between trust and interest in usage behavior, have no significant effect.

Empowering and Disempowering Motivational Climates, Mediating Psychological Processes, and Future Intentions of Sport Participation

Based on the conceptual model of multidimensional and hierarchical motivational climate the objective of this study was to test two models. One model (M1) of total mediation, testing the mediating mechanisms that explain why the motivational climate affects intention of continuity or dropout. Specifically, we test the mediating role of satisfaction/frustration of basic psychological needs and self-determined motivation, in the relationship between the players’ perception of the empowering and disempowering climate created by the coach, and the intention of young soccer players to continue/dropout the sport practice. The second model (M2) of partial mediation, contributes to knowing the mechanisms that link the antecedent variables included in the model (perceived empowering and disempowering motivational climate) and the outcomes (intention of continuity or dropout in sport). A total of 381 young male soccer players between 12 and 14 years of age (M = 12.41, SD = 0.89), completed a questionnaire package tapping into the variables of interest: players’ perception of the motivational climate created by the coach (empowering and disempowering), satisfaction/thwarting of basic psychological needs, self-determined motivation and the intention to continue/dropout sports participation. The hypothesized model was tested using a structural equation model technique with latent variables. The results of the partial mediation model were satisfactory (χ2= 120.92; df = 68; RMSEA = 0.045; CFI = 0.968; TLI = 0.957) and showed that need satisfaction and self-determined motivation partially mediated the relationship between the perception of the empowering climate and the intention to continue. Moreover, need satisfaction showed a positive and significant relationship with the intention to continue sports participation. Additionally, need thwarting and self-determined motivation totally mediated the relationship between the perception of the disempowering climate and the intention to dropout. Furthermore, needs thwarting was positively and significantly related to the intention to dropout of sports participation. Findings point to the importance of fostering empowering climates and preventing the creation of disempowering climates in the grassroots football.

Influence of Resilience on Burnout Syndrome of Faculty Professors

This research aims to describe the relationship between resilience and burnout facing COVID-19 pandemics. The sample was n = 831 lecturers and professors of a Mexican public university. This study is a quantitative, non-experimental, cross-sectional, explanatory, and ex post facto research using Structural Equations Modeling with latent variables under the partial least square’s method technique. We used the CD-RISC-25 and SBI questionnaires to measure resilience and burnout, respectively. Structural Equations Modeling (SEM–PLS) allowed the visualization of the exogenous variable (resilience) in endogenous variables (dimensions of SBI burnout: E9 guilt, E7 emotional exhaustion, E8 indolence, and E6 work illusion). To this day, there are very few previous studies that jointly analyze in Mexico the characteristics of resilience and burnout in the face of the COVID-19 pandemic. Findings show that resources availability has the strongest correlation with accomplishment in teaching, followed by cynicism and emotional exhaustion. These results have important professional implications.

Neural Circuits for Dynamics-Based Segmentation of Time Series

The brain must extract behaviorally relevant latent variables from the signals streamed by the sensory organs. Such latent variables are often encoded in the dynamics that generated the signal rather than in the specific realization of the waveform. Therefore, one problem faced by the brain is to segment time series based on underlying dynamics. We present two algorithms for performing this segmentation task that are biologically plausible, which we define as acting in a streaming setting and all learning rules being local. One algorithm is model based and can be derived from an optimization problem involving a mixture of autoregressive processes. This algorithm relies on feedback in the form of a prediction error and can also be used for forecasting future samples. In some brain regions, such as the retina, the feedback connections necessary to use the prediction error for learning are absent. For this case, we propose a second, model-free algorithm that uses a running estimate of the autocorrelation structure of the signal to perform the segmentation. We show that both algorithms do well when tasked with segmenting signals drawn from autoregressive models with piecewise-constant parameters. In particular, the segmentation accuracy is similar to that obtained from oracle-like methods in which the ground-truth parameters of the autoregressive models are known. We also test our methods on data sets generated by alternating snippets of voice recordings. We provide implementations of our algorithms at https://github.com/ttesileanu/bio-time-series.

Estimating Distributions of Parameters in Nonlinear State Space Models with Replica Exchange Particle Marginal Metropolis–Hastings Method

Extracting latent nonlinear dynamics from observed time-series data is important for understanding a dynamic system against the background of the observed data. A state space model is a probabilistic graphical model for time-series data, which describes the probabilistic dependence between latent variables at subsequent times and between latent variables and observations. Since, in many situations, the values of the parameters in the state space model are unknown, estimating the parameters from observations is an important task. The particle marginal Metropolis–Hastings (PMMH) method is a method for estimating the marginal posterior distribution of parameters obtained by marginalization over the distribution of latent variables in the state space model. Although, in principle, we can estimate the marginal posterior distribution of parameters by iterating this method infinitely, the estimated result depends on the initial values for a finite number of times in practice. In this paper, we propose a replica exchange particle marginal Metropolis–Hastings (REPMMH) method as a method to improve this problem by combining the PMMH method with the replica exchange method. By using the proposed method, we simultaneously realize a global search at a high temperature and a local fine search at a low temperature. We evaluate the proposed method using simulated data obtained from the Izhikevich neuron model and Lévy-driven stochastic volatility model, and we show that the proposed REPMMH method improves the problem of the initial value dependence in the PMMH method, and realizes efficient sampling of parameters in the state space models compared with existing methods.