Investor attention and corporate social responsibility of family businesses in Vietnam: The moderating role of CEO overpower.

This study examines the influence of investor attention and Chief Executive Officers (CEOs) power on Corporate Social Responsibility (CSR) within Vietnamese family businesses. Unlike most of the past literature, this study further investigates the potential moderating effects of CEOs' power on the relationship between investor attention and CSR. Utilizing the dynamic system Generalized Method of Moments (GMM), this study analyzes a dataset comprising 116 Vietnamese family businesses from 2005 to 2020. The findings reveal an inverted U-shape between CEO power and CSR within family businesses; meanwhile, investor attention demonstrates a negative impact on CSR. Moreover, the results report that CEO power is a moderating factor in the relationship between investor attention and CSR. These results are consistent with various theoretical frameworks, including agency theory, overinvestment, career concern, career horizon, and conflict-resolution hypotheses. Finally, our study offers management implications to foster the sustainable development of CSR within family businesses, particularly within emerging markets.

The trilogy between CEO overpower, green credit, and core competence: Evidence from commercial banks in Vietnam.

This paper investigates the interconnection between Chief Executive Officer (CEO) power, green credit, and core competence of commercial banks in Vietnam. Our data sample consists of 373 annual observations from 2010 to 2021. We employ a dynamic system Generalized Method of Moments to analyze an unbalanced panel comprised of 373 annual observations from 2010 to 2021. The findings indicate an inverse U-shape relationship between CEO overpower and commercial banks' core competence. Moreover, the study reports that banks with green lending activities reduce core competence by about 0.1598 points more than other banks. In addition, the results indicate that CEO power moderates the relationship between green credit and core competence. Our findings align with stewardship, management entrenchment, first-mover advantage, stakeholder theories, and prior literature. The study has practical implications for policymakers to develop the banking system sustainably in emerging markets.

Adaptive Accumulation of Plantar Pressure for Ambulatory Activity Recognition and Pedestrian Identification.

In this paper, we propose a novel method for ambulatory activity recognition and pedestrian identification based on temporally adaptive weighting accumulation-based features extracted from categorical plantar pressure. The method relies on three pressure-related features, which are calculated by accumulating the pressure of the standing foot in each step over three different temporal weighting forms. In addition, we consider a feature reflecting the pressure variation. These four features characterize the standing posture in a step by differently weighting step pressure data over time. We use these features to analyze the standing foot during walking and then recognize ambulatory activities and identify pedestrians based on multilayer multiclass support vector machine classifiers. Experimental results show that the proposed method achieves 97% accuracy for the two tasks when analyzing eight consecutive steps. For faster processing, the method reaches 89.9% and 91.3% accuracy for ambulatory activity recognition and pedestrian identification considering two consecutive steps, respectively, whereas the accuracy drops to 83.3% and 82.3% when considering one step for the respective tasks. Comparative results demonstrated the high performance of the proposed method regarding accuracy and temporal sensitivity.

Daily wrist activity classification using a smart band.

OBJECTIVE: In this letter, we propose a novel method for classifying daily wrist activities by using a smart band. APPROACH: Triaxial acceleration data are collected by built-in sensors of the smart band during experiments regarding five activities, i.e. texting, calling, placing a hand in a pocket, carrying a suitcase, and swinging a hand. We analyze patterns in the sensor signals during these activities based on three types of features, i.e. norm, norm-variance, and frequency-domain features. After extracting the significant features, a multi-class support vector machine algorithm is applied to classify these activities. MAIN RESULTS: We obtained recognition error rates of approximately 2.7% by applying the proposed method to the experimental dataset.

Step-Detection and Adaptive Step-Length Estimation for Pedestrian Dead-Reckoning at Various Walking Speeds Using a Smartphone.

We propose a walking distance estimation method based on an adaptive step-length estimator at various walking speeds using a smartphone. First, we apply a fast Fourier transform (FFT)-based smoother on the acceleration data collected by the smartphone to remove the interference signals. Then, we analyze these data using a set of step-detection rules in order to detect walking steps. Using an adaptive estimator, which is based on a model of average step speed, we accurately obtain the walking step length. To evaluate the accuracy of the proposed method, we examine the distance estimation for four different distances and three speed levels. The experimental results show that the proposed method significantly outperforms conventional estimation methods in terms of accuracy.

Stride Counting in Human Walking and Walking Distance Estimation Using Insole Sensors.

This paper proposes a novel method of estimating walking distance based on a precise counting of walking strides using insole sensors. We use an inertial triaxial accelerometer and eight pressure sensors installed in the insole of a shoe to record walkers' movement data. The data is then transmitted to a smartphone to filter out noise and determine stance and swing phases. Based on phase information, we count the number of strides traveled and estimate the movement distance. To evaluate the accuracy of the proposed method, we created two walking databases on seven healthy participants and tested the proposed method. The first database, which is called the short distance database, consists of collected data from all seven healthy subjects walking on a 16 m distance. The second one, named the long distance database, is constructed from walking data of three healthy subjects who have participated in the short database for an 89 m distance. The experimental results show that the proposed method performs walking distance estimation accurately with the mean error rates of 4.8% and 3.1% for the short and long distance databases, respectively. Moreover, the maximum difference of the swing phase determination with respect to time is 0.08 s and 0.06 s for starting and stopping points of swing phases, respectively. Therefore, the stride counting method provides a highly precise result when subjects walk.