Adoption of Automatic Warehousing Systems in Logistics Firms: A Technology–Organization–Environment Framework

Abstract

The adoption of automatic warehousing systems, a type of green technology, has been an emerging trend in the logistics industry. In this study, we develop a conceptual model using a technology–organization–environment framework to investigate the factors which influence logistics firms to adopt green technology. Our model proposes that the adoption of green technology is influenced by perceived advantage, cost, technological turbulence, business partner influence, firm size, firm scope and operational performance. The objective of this study is to identify the conditions, as well as the contributing factors, for the adoption of automatic warehousing systems in logistics firms. Data were collected from 98 firms in China, and structural equation modeling with partial least squares is adopted to analyze the data. The results suggest that high perceived relative advantage, firm size, cost, firm scope, operation performance, technological turbulence and influence of business partners are important factors affecting IT adoption in small businesses. Therefore, decision support should be provided for enterprises from the three aspects of technology, organization and environment to improve the adoption of automatic warehousing systems.

Improving the Operational Efficiency of Parcel Delivery Network with a Bi-Level Decision Making Model

“…Although the Poisson distribution assumption offers a convenient mathematical solution, the shipment arrivals could be distributed differently. Furthermore, the model can be more accurate if we consider an actual shipment handling process in a terminal, such as inventory holding [1,37], sorting [15,19], and loading and unloading [7]. Our study conducted a sensitivity analysis to quantify the tradeoff relationship between transport costs and terminal costs.…”

“…In other words, if the terminal H1 is busy, assigning some shipments of subterminal A to H3 can reduce the congestion of H1 and decrease the total terminal cost. The other solution is to improve the processing rate of a terminal (i.e., H1) by increasing the number of employees or adding processing machinery, or automatic warehousing systems [19]. The planner determines the processing rate of a terminal (C a cri ) to minimize the total processing cost.…”

“…If an arrival rate of shipments is less than the critical service rate, the unit cost of terminal processing is a base rate. Otherwise, a processing cost increases linearly, as shown in Equation (19). π is empirically determined from our experiments based on Equation 16.…”

“…The base processing rate is assumed as 85% of the maximum processing rate (0.15 processing margin shown in Table 1). The shipment arrival rate of the base case is computed from the hierarchical relationship between sub and hub terminals, which also affects processing cost from Equation (19). We analyze the proposed model by comparing it with other strategies in addition to the base case (Table 3).…”

Task Scheduling Problem of Double-Deep Multi-Tier Shuttle Warehousing Systems

“…In the literature reviews, most researchers have focused on carbon emissions in warehousing design and path optimization and storage policies [8][9][10][11]. Some scholars have established novel models to study the sustainability of warehousing systems [12,13]. In terms of task scheduling, there have been few studies on carbon emissions.…”