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Online portfolio selection sequentially allocates wealth among a set of assets and aims to maximize the investor’s cumulative return in the long run. Various existing algorithms in the finance and accounting area adopt an indirect approach to exploit one asset characteristic through the channel of assets’ expected return and thus cannot fully leverage the power of various asset characteristics found in the literature. This study aims to propose new algorithms to overcome this issue to enhance investment performance.

We propose a parameterized portfolio selection (PPS) framework, which directly incorporates multiple asset characteristics into portfolio weights. This framework can update parameters timely based on final performance without intermediate steps and produce efficient portfolios. We further append L1 regularization to constrain the number of active asset characteristics. Solving the PPS formulation numerically, we design two online portfolio selection (OLPS) algorithms via gradient descent and alternating direction method of multipliers.

Empirical results on five real market datasets show that the proposed algorithms outperform the state of the arts in cumulative returns, Sharpe ratios, winning ratios, etc. Besides, short-term characteristics are more important than long-term characteristics, and the highest return category is the most important characteristic to improve portfolio performance.

The proposed PPS algorithms are new end-to-end online learning approaches, which directly optimize portfolios by asset characteristics. Such approaches thus differ from existing studies, which first predict returns and then optimize portfolios. This paper provides a new algorithmic framework for investors’ OLPS.

This study reveals the trigger of innovative behavior from the perspective of intrinsic and extrinsic spiritual inspiration and provides a new research idea for the formation mechanism of innovative behavior. The purpose of this study is to provide certain guidance and implications for enterprises to cultivate and enhance employees’ innovative behavior.

We conducted three studies, collected multi-source data (N = 1,175) from different countries longitudinally, as well as used hierarchical regression analysis and fuzzy-set quantitative comparative analysis to verify the theoretical model.

According to the findings, both spiritual leadership and career calling have a positive impact on employees’ innovative behavior through the mediating effect of autonomous motivation and the moderating effect of person-vocation fit.

Innovative behavior is the positive professional pursuit of employees, which is difficult to form without the motivation of spiritual factors. Spirituality is a complex concept that contains intrinsic and extrinsic spiritual factors, both of which could stimulate employees’ innovative behavior. Although many discussions have been held on this topic in recent years, little attention has been paid simultaneously to the motivating effects of the two perspectives. Drawn from self-determination theory, this study explores the mechanisms of two spiritual motivation paths (i.e. the intrinsic and extrinsic spiritual motivation paths) in the improvement of employees’ innovative behavior.

This study aims to investigate the optimal pricing decisions and shared channel strategy selection of battery manufacturers considering heterogeneous consumers' range anxiety.

Amidst the rapid growth of the electric vehicle sector, countries are promoting upgrades in the automotive industry. However, insufficient driving range causes consumer range anxiety. The study utilizes the Stackelberg game model to assess how range anxiety influences battery manufacturers' pricing and channel strategy decisions across three strategies.

We find that electric vehicle battery manufacturers' decisions to cooperate with third-party sharing platforms (TPSPs) are primarily influenced by fixed costs and consumer range anxiety levels. As range anxiety increases, the cost threshold for joining shared channels rises, reducing cooperation likelihood. However, considering diverse consumer needs, especially a higher proportion of leisure-oriented consumers, increases the likelihood of cooperation. Furthermore, higher battery quality makes direct participation in shared channels more probable.

In the electric vehicle industry, range anxiety is a significant concern. While existing literature focuses on its impact on consumer behavior and charging infrastructure, this study delves into battery manufacturers' strategic responses, offering insights into channel options and pricing strategies amidst diverse consumer segments.

This research aims to improve the competence of the task force of vocational higher education study programs in preparing a proposal for the Vocational Higher Education Strengthening Program-Private University (VHESP-PU) of the Independent Campus Program.

This type of research is R&D by applying the ADDIE model procedures. The research subjects were 30 lecturers assigned as study program task forces from 15 private universities in North Sumatra. The techniques to collect data are tests and questionnaires. Data analysis techniques use quantitative descriptive statistics.

The application of the ADDIE Model was proven effective in achieving training objectives based on the mean comparison analysis between the pre-test and post-test, showing an increase. The maximum score achieved by participants also increased by 9.52% from 84 to 92. Participants' evaluation of the implementation of the training was also dominant in the “Good” and “Very Good” categories. The training learning achievements that participants have obtained will help prepare higher-quality VHESP-PU proposals for each fiscal year to optimize training sustainability.

The fact that only 15 universities participated in the training underscores the urgent need for ongoing training using the ADDIE model. This will ensure that more participants from private universities in North Sumatra can benefit from this practical approach to proposal preparation.

Using the ADDIE model to prepare VHESP-PU proposals is an innovative approach that can inspire similar applications in other fields. This research demonstrates that the ADDIE model, usually associated with instructional design, curriculum or learning materials, can also effectively prepare funding proposals using a competition scheme.

The use of the ADDIE model in preparing VHESP-PU proposals is an innovation because ADDIE is usually more related to developing instructional design, curriculum or learning materials rather than funding proposals using a competition scheme.

The study aims to investigate the structural relationship between motivation, involvement, satisfaction and loyalty in the context of hiking tourism in Hong Kong.

This study develops a comprehensive conceptual framework that integrates motivations, satisfaction, loyalty and involvement. A convenience survey of hikers (n = 859) was conducted in Tai Mo Shan Country Park in Hong Kong, from December 2020 to May 2021. The data were analyzed using structural equation modeling.

The results indicated that stimulus–avoidance motive and involvement had direct effects on satisfaction. Stimulus–avoidance, social and competence–mastery motives were correlated with involvement which indirectly affected satisfaction. Loyalty was directly affected by satisfaction and involvement.

This study contributes to the existing literature by filling the research gap in hiking tourism. It not only provides a novel understanding of tourist behavior but also offers valuable implications for improving hiking experiences and meeting the needs of hikers effectively.

Using a 62,742-ha working forest in New Brunswick, Canada, we examine the benefit and cost of carbon additionality at the landscape level.

The baseline scenario is set to maximize timber profit over a 100-year planning period, whereas the carbon scenario is set to have a 5- or 10-year rotation extension.

At a carbon price of $8/tCO₂e, the benefit of additional carbon sequestration from the working forest cannot offset its cost. For the benefit-cost ratio to be one, the respective break-even price needs to be $21/tCO₂e for the 5-year rotation extension and $25/tCO₂e for the 10-year rotation extension.

This study analyzes the carbon additionality and economics of working forests at the 50–100 thousand hectare scale. Specifically, we examine the change in benefit and cost between a baseline scenario of timber management only and a scenario of rotation extension for both timber and carbon sequestration.

Driven by the rapid expansion of online retail and the surge in livestream commerce, the impact of different livestream mode on brand and platform performance has become a critical issue. This paper analyzes the impact of artificial intelligence (AI) and key opinion leader (KOL) livestream on the profitability of brands and the platform, incorporating the effects of horizontal interactions to identify the optimal livestream mode.

This paper develops a model of a platform supply chain involving two brands and a platform, where each brand independently decides whether to utilize KOL or AI livestream. Applying Stackelberg game approach, the study derives equilibria for various livestream scenarios, identifying the optimal livestream mode for both parties. Additionally, the model is extended to incorporate asymmetric market potential and network externality to evaluate their impact on a brand’s choice of livestream mode.

Several interesting and important results are derived in this paper. Firstly, it is found that AI livestream enables brands to leverage network externality and mitigate the market disadvantage, thereby gaining a competitive advantage. Secondly, while KOL livestream promotes trust, the medium KOL commission rates could cause brands to be trapped in a prisoner’s dilemma, and excessively high commission rates may render them less profitable. Thirdly, the KOL commission rate, network externality intensity, horizontal interactions and market disadvantage are critical determinants influencing a brand’s choice of livestream mode.

This study is the first to investigate the effects of horizontal interactions, asymmetric market potential and asymmetric network externality on livestream mode selection by brands within a platform supply chain. The research provides valuable insights into optimizing livestream strategies to enhance brand profitability.

The optimization of transport efficiency by self-operated logistics has brought competitive advantages to platform, who is gradually developing self-operated logistics and adopting the preannouncement to announce the related information in advance. The purpose of this paper is to explore the development order of self-operated logistics on platform under consideration of preannounce behavior.

This paper considers the sequence of platform constructing the self-operated logistics and constructs the two-stage pricing models to analyze the optimal pricing of platforms under different preannounce strategies, including four scenarios: {no-preannounce, first mover}, {no-preannounce, second mover}, {preannounce, first mover} and {preannounce, second mover}.

The authors receive several conclusions: First, under no-preannounce scenario, regardless of the sequence of entry into self-operated logistics market, when the quality differentiation of two platforms’ self-operated logistics is moderate, the ratio pricing of two platforms at competition stage is positively correlated with quality differentiation of their self-operated logistics. Additionally, there exists the substitution effect between preannouncement and quality differentiation under no-preannounce condition, and the first-mover platform should increase the pricing of the monopoly phase until it is twice as high as its pricing during the competition phase. Interestingly, the pricing of platform and the strategy for developing self-operated logistics are symmetric between first- and second-mover scenarios.

First, this study analyzes the pricing and self-operated logistics construction under different preannounce strategies, enriching the interdisciplinary research on corporate marketing and providing scientific suggestions on how to use preannouncement to acquire competitive advantages. Second, this paper also considers the sequence of platform developing self-operated logistics and analyzes how platform develops self-operated logistics as well as pricing to gain first-mover and second-mover advantages. Third, this paper develops the two-stage pricing models that consider the continuity of pricing in different cycles, enriching the relevant theories and models.

The application of industrial robots in modern production is becoming increasingly widespread. In the context of flexible production lines, quickly and accurately identifying and grasping specified workpieces is particularly important. This study aims to propose a grasping scheme that combines traditional methods with deep learning to improve grasping accuracy and efficiency.

First, a dataset generation method is proposed, which constructs a point cloud dataset close to the real scene without the need for extensive data collection. Then, the 3D object detection algorithm PointPillars is improved based on the features of the scene point cloud, allowing for the analysis of part poses to achieve grasping. Finally, a grasp detection strategy is proposed to match the optimal grasp pose.

Experimental results show that the proposed method can quickly and easily construct high-quality datasets, significantly reducing the time required for preliminary preparation. Additionally, it can effectively grasp specified workpieces, significantly improving grasping accuracy and reducing computation time.

The main contribution of this paper is the integration of a novel dataset generation method, improvements to the PointPillars algorithm for 3D object detection and the development of an optimal grasp detection strategy. These advancements enable the grasping system to handle real-world scenarios efficiently and accurately, demonstrating significant improvements over traditional methods.

This paper aims to offer a comprehensive review and categorization of production optimization throughout the additive manufacturing lifecycle in a cloud environment. It aims to provide a structured approach to identifying and addressing issues.

This paper systematically reviews 75 technical papers on cloud manufacturing, nesting, scheduling and postprocessing in additive manufacturing. This includes a detailed discussion of the key issues.

This paper introduces a production framework for the entire lifecycle of additive manufacturing in a cloud environment. This framework aids in problem identification and decision-making based on the process flow. It provides an integrated view from cloud to postprocessing, examining decision interdependencies and enhancing problem identification and organization.

To the best of the authors’ knowledge, this paper is the first to review the complete lifecycle of additive manufacturing, emphasizing the often-overlooked aspects of postprocessing and cloud manufacturing. It offers a comprehensive study of lifecycle optimization challenges and suggests ways to streamline the production process.