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This article examines the impact of banks’ corporate social responsibility communication through social media (CSR-S), electronic word of mouth (eWOM), and brand reputation on consumer behavior during the COVID-19 crisis, with a focus on purchase intention.

The study employed a quantitative approach to analyze data from a survey of 621 Egyptian bank customers who followed the banks’ social media pages and interacted with CSR-S initiatives. A genetic algorithm selected the most relevant variables affecting purchase intention. A Bayesian regression model was used to analyze the impact of CSR-S communication, eWOM, and brand reputation on purchase intention.

CSR-S initiatives, eWOM, and brand reputation were found to influence customer purchase intention. CSR-S initiatives can boost purchase intention by encouraging brand reputation and initiative sharing with friends and other customers. However, CSR-S negatively moderates the positive impact of eWOM and brand reputation on the predisposition to contract products and services with the bank.

This study addresses critical research gaps in CSR literature. Firstly, it examines the impact of CSR-S actions on customer behavior, a perspective less explored in previous research. Secondly, it investigates the intricate relationships between CSR-S, eWOM, brand reputation, and purchase intention, shedding light on their interplay, particularly during the COVID-19 pandemic. Additionally, this research extends CSR-S investigations to the competitive banking industry and focuses on a developing country context, enhancing the applicability of findings for Egyptian banks. Lastly, the study employs advanced methodologies to improve the accuracy of results.

本文擬探討於2019冠狀病毒病危機期間、銀行透過社交媒體而進行關於企業社會責任的溝通 (以下簡稱社媒企社責溝通) 、電子口碑和品牌聲譽,如何影響消費行為; 研究會聚焦於客戶的購買意向上。

研究以定量方法、去分析來自涵蓋621名埃及銀行客戶的調查的數據; 這些客戶均有追隨銀行的社交媒體頁面,並曾與銀行就企業社會責任提出的倡議進行互動交流。研究人員以基因演算法挑選了與購買意向相關性最密切的變量,並以貝葉斯回歸模型,去分析探討社媒企社責溝通、電子口碑和品牌聲譽、如何影響客戶的購買意向。

研究結果顯示,透過社交媒體傳達的企業社會責任倡議、電子口碑和品牌聲譽,均會影響客戶的購買意向。這類倡議會透過促進品牌聲譽和朋友或客戶間的互相共享而令購買意向提昇。唯社媒企社責溝通會減弱電子口碑和品牌聲譽給客戶購買意向帶來的正面影響,使他們與銀行訂立商品或服務契約的意欲降低。

本研究致力回應企業社會責任文獻內重要的研究空白。首先,研究人員探討社媒企社責溝通對客戶行為帶來的影響,這研究角度從來沒有被充分利用。其次,本研究探討社媒企社責溝通、電子口碑、品牌聲譽和購買意向之間錯綜複雜的關係,這幫助闡明各元素的相互作用,尤以2019冠狀病毒病肆虐期間為甚。再者,本研究把關於社媒企社責溝通的研究擴展至競爭性銀行業,並聚焦於涉及一個發展中國家的背景,這都使研究結果更能應用於分析埃及銀行上。最後,研究人員為了提高研究結果的準確性,採用了先進的方法進行研究。

The purpose of this paper is to investigate the vehicle-based sensor effect and pavement temperature on road condition assessment, as well as to compute a threshold value for the classification of pavement conditions.

Four sensors were placed on the vehicle’s control arms and one inside the vehicle to collect vibration acceleration data for analysis. The Analysis of Variance (ANOVA) tests were performed to diagnose the effect of the vehicle-based sensors’ placement in the field. To classify road conditions and identify pavement distress (point of interest), the probability distribution was applied based on the magnitude values of vibration data.

Results from ANOVA indicate that pavement sensing patterns from the sensors placed on the front control arms were statistically significant, and there is no difference between the sensors placed on the same side of the vehicle (e.g., left or right side). A reference threshold (i.e., 1.7 g) was computed from the distribution fitting method to classify road conditions and identify the road distress based on the magnitude values that combine all acceleration along three axes. In addition, the pavement temperature was found to be highly correlated with the sensing patterns, which is noteworthy for future projects.

The paper investigates the effect of pavement sensors’ placement in assessing road conditions, emphasizing the implications for future road condition assessment projects. A threshold value for classifying road conditions was proposed and applied in class assignments (I-17 highway projects).

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.