Crowd-Sourcing a Way to Sustainable Urban Logistics: What Factors Influence Enterprises’ Willingness to Implement Crowd Logistics?

Abstract

The sharing economy has brought new opportunities to the logistics industry, which has created an emerging trend known as crowd logistics. Implementing this mode offers a basis for more sustainable urban logistics, but there is limited research on what leads enterprises to adopt crowd logistics. Based on the technology-organization-environment (TOE) theoretical model, this paper developed a model to study the influencing factors of enterprises' willingness to implement crowd logistics. The data were collected through questionnaire surveys, SPSS and AMOS were used for data analysis. The empirical results showed that the relative advantage, absorptive capacity, market environment, and external motivations have significant positive impact on the company's willingness to implement crowd logistics, while complexity and resources have no significant impact. Crowd logistics offers an important route to more sustainable urban logistics. Logistics enterprises should take measured steps when implementing crowd logistics, improve their absorptive capacity, and take necessary precautions towards minimizing the risks of crowd logistics.

The Influence of Trust on Crowd Logistics Enterprise’s Operational Performance: A SEM-PLS Model

“…e research method of this paper consists of five steps: (1) questionnaire design, (2) questionnaire pretest, (3) data collection, (4) selection of control variables, and (5) data analysis [37,64].…”

“…For example, studies have been made analyzing the influencing factor affecting continued participation in crowd logistics, from the perspective of crowd workers (e.g., Ye and Kankanhalli [9], Liang et al [33], and Huang et al [37]). Several studies have investigated the influencing factors of implementation from the perspective of enterprises or platforms (e.g., Yao et al [7], Bin et al [32], and Bin et al [64]). Some have explored the environmental sustainability of crowd logistics (e.g., Mladenow et al [6], Rai et al [28], and Lin et al [31]).…”

Demand Forecasting for Rural E-Commerce Logistics: A Gray Prediction Model Based on Weakening Buffer Operator

“…Mobile Information Systems information and small sample size, and the system will change with time and situation. e system of rural logistics demand is characterized by lack of research information and small samples [35], and the system will change with the time and conditions [36,37]. e gray prediction model can forecast the uncertainty system with known and unknown information, its prediction accuracy is high, and it is suitable for short and medium-term prediction [38].…”

Application of Flipped Classroom in the Era of Big Data: What Factors Influence the Effect of Teacher‐Student Interaction in Oral English Teaching

“…In addition, the software can be used for multifactor causality studies [33]. 3, Cronbach's alpha (CA), combined reliability (CR), and average extraction variance (AVE) values of the five latent variables all reached the relevant threshold [34,35]. Meanwhile, in Figure 2, the external load values for each of the observed variables are greater than 0.7.…”