Zongwei Li

Purpose: Digital transformation (DT) is a configurational construct spanning technology, process, organizational, and strategic change, and valid measurement is a prerequisite for understanding it. Existing text-based methods fragment annual reports into keywords or isolated sentences, breaking the cross-sentence semantic structure that expresses the construct. This paper examines how textual representational granularity affects content validity in DT measurement, and proposes a complete-semantic measurement framework. Design/methodology/approach: From 28,823 annual reports of Chinese listed manufacturers (2006–2023), we build a 1,000-report sample through structured sampling and design a three-stage complete-semantic framework (input, training-signal, and model-bearing). The framework is evaluated through comparative experiments, ablation, and sensitivity analysis, then applied to the full sample, with an existing sentence-level measure used for external validation. Findings: Textual representational granularity shapes which forms of the construct enter measurement: a fragmented representation relies on explicit digital keywords and leaves early and implicit transformation unseen. Across the full sample, DT in Chinese manufacturing has widened in breadth and deepened in structure, shifting from localized exploration to cross-departmental integration. An existing sentence-level measure agrees on explicit, deep-stage transformation but diverges in the early stages that fragmentation struggles to capture. Research limitations: The study focuses on Chinese manufacturing; its conclusions need testing in other industries, settings, and disclosure contexts, and on new model architectures and cross-construct tasks. Practical implications: The framework requires little annotation, supports local deployment, and aligns with national standards, offering a reproducible tool for research, policy, and firm-level analysis. Originality/value: The contribution lies not in using large language models (LLMs) for text classification, but in integrating the cross-sentence semantic structure on which a configurational construct depends. The paper brings textual representational granularity into content-validity assessment, extending construct validity from “item–construct domain” coverage to “textual representation–construct structure” alignment, and offering a testable lens for measuring complex organizational constructs from disclosure text.

Single-wave order picking in dynamic warehouses is a sequential multi-goal navigation problem. A robot must visit an ordered set of shelves and then a delivery station while avoiding moving obstacles under partial observability. Existing approaches either entangle long-horizon task logic with low-level obstacle avoidance or rely on static-environment assumptions that limit responsiveness in dynamic settings. This paper proposes the Single-Wave Dynamic Warehouse Navigation System (SW-DWNS), a lightweight scheduling framework that extends a pretrained ColorDynamic point-to-point local planner to ordered warehouse picking without retraining. The scheduler maintains a shelf queue, exposes only the active subgoal to the local policy, and advances the queue through an arrival-gated state machine with a geometrically derived pickup threshold. We evaluate SW-DWNS in a controlled dynamic warehouse simulation benchmark with three layouts, pickup horizons K ∈ {1, …, 5}, stochastic obstacle motion, sensor noise, and obstacle-size randomization. Across the full layout–horizon grid, SW-DWNS achieves 87.3% average success, compared with 26.8% for A* search combined with the Dynamic Window Approach (A* + DWA) and 1.3% for ColorDynamic-only. The gain is not obtained by sacrificing safety: the average collision rate is reduced from 69.0% for A* + DWA to 7.5% for SW-DWNS. Factor-wise ablation shows uneven robustness across disturbances: sensing noise and obstacle-size randomization have limited impact (main effects of +1 and +4.5 percentage points), whereas disabling obstacle motion costs 33 percentage points and shifts failures from collision to timeout. A pickup-threshold sweep further shows that reported success rates become inflated outside the admissible geometric band ε ∈ [14, 31] cm. These results show that a decoupled scheduling layer can make a pretrained dynamic local planner effective for sequential warehouse picking, while also revealing the conditions under which that extension remains valid.

This study proposes an AI- and big data–driven framework for modeling acoustic features to predict consumer purchasing behavior in e-commerce live streaming. Using large-scale data collected from the Douyin platform, the study systematically extracts three key acoustic features—loudness, pitch, and timbre—through the Librosa library. An XGBoost regression model is employed to evaluate the predictive power of these features, while consumer recognition (measured by likes per minute) is introduced as a mediating variable to examine its indirect effects. The empirical results reveal that acoustic features significantly predict consumer purchasing behavior, with loudness exerting the strongest influence. Moreover, consumer recognition mediates the relationship between streamers’ acoustic characteristics and sales performance. The proposed framework demonstrates strong reproducibility and scalability, providing a technical foundation for multimodal behavior modeling and intelligent marketing applications in live-streaming commerce.

Purchase intention and decision turn out to be the research hotspots in the field of consumer behavior, whereas the reasons for consumer hesitation before purchase have been scarcely investigated. In order to explore the factors that affect consumers’ online purchase hesitation in the context of e-commerce retail, this study uses the theory of perceived risk to analyze how different dimensions of perceived risk dominated by consumer trust affect consumers’ purchase hesitation at the checkout stage, examines the moderating effect of consumers’ online shopping experience on the influencing process, and builds a test model with moderating variables. In this study, the factors for purchase hesitation are empirically analyzed using the methods of literature analysis and logistic regression based on the real transaction data from Taobao. The results show that seller credit rating, representing seller risk, exerts a significant adverse effect on purchase hesitation, whereas the order amount (i.e. financial risk, and price dispersion, representing product risk) positively affects purchase hesitation. The consumer experience plays a moderating role in the above process. In addition, this article further explores the heterogeneous role of buyer’s device, seller’s platform, and buyer’s gender and excludes some of the interfering factors that lead to involuntary hesitation. JEL Classification: M31, M37

Nowadays, the commercial potential of live e-commerce is being continuously explored, and machine vision algorithms are gradually attracting the attention of marketers and researchers. During live streaming, the visuals can be effectively captured by algorithms, thereby providing additional data support. This paper aims to consider the diversity of live streaming devices and proposes an extremely lightweight and high-precision model to meet different requirements in live streaming scenarios. Building upon yolov5s, we incorporate the MobileNetV3 module and the CA attention mechanism to optimize the model. Furthermore, we construct a multi-object dataset specific to live streaming scenarios, including anchor facial expressions and commodities. A series of experiments have demonstrated that our model realized a 0.4% improvement in accuracy compared to the original model, while reducing its weight to 10.52%.

Implementing green transition with high quality has been confirmed as a critical approach to achieve sustainable development worldwide. This study focuses on how “Internet plus” affects the green transition of manufacturing companies. Based on the research sample of A-share-listed manufacturing companies from 2015 to 2020, the study concludes that “Internet plus” can significantly improve green transition, and the reliability of the above results is confirmed through endogeneity analysis and robustness tests. The result of the heterogeneity analysis suggests that “Internet plus” has a more significant promotion effect on state-owned companies, eastern regions, and high-tech industries. The result of the in-depth analysis indicates that “Internet plus” can contribute to green transition by boosting green innovation. In addition, environmental regulation and sewage fee-to-tax have significant positive effects in regulating the above relationships, further confirming that the enactment of the Environmental Protection Tax increases the regulating effect. This study provides a new theoretical framework for the intrinsic mechanism of “Internet plus” affecting the green transition of manufacturing companies.