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This investigation seeks to elaborate on how proactive market orientation (MO) and responsive MO motivate firms to conduct business model innovation (BMI) through the breadth of market knowledge search (BMKS) and the depth of market knowledge search (DMKS).

Based on the survey data of 259 high-tech firms in the industrial parks of the Yangtze River Delta, this study uses multiple hierarchical regressions to examine the hypotheses and conducts Sobel and bootstrapping methods to further test the mediating effects.

The findings indicate that the positive effects of proactive and responsive MO on BMI are mediated by BMKS and DMKS. It also shows that proactive MO has a greater impact on BMKS than responsive MO, while responsive MO has a stronger impact on DMKS than proactive MO.

Firms with different MOs can choose different types of market knowledge search to promote BMI, which reminds managers to give attention to the importance of bridging MOs with knowledge search strategies in BMI.

This study introduces a constructive theoretical framework by examining the roles of MO and market knowledge search on BMI. The findings reveal that MO as a key initiating factor and market knowledge search as an important conduit play vital roles in the experimental process of BMI and identify the differential effects of proactive and responsive MO on two types of market knowledge search.

Drawing from the Personal Construct Theory, this study aims to analyze the impact of using gamified apps on user behavior by investigating the service-related images and individual preferences of Generation Z (GenZ) consumers, as these emerge from gamified applications in a tourism context.

The repertory grid analysis (RGA) elicited the top elements that reflect GenZer perceptions in tourism from empirical studies in the UK and Greece. Generalized procrustes analysis was used to investigate the structure of the data for the creation of representative consensus biplots of the most important conceptual constructs to advance consumer decision-making modeling via gamification.

As per different gamified app best-practices considered, the authors extract not only common perceptual elements (e.g. place informative aspects, exploration, lodgings, food/catering) but also different image components (e.g. virtual/interactive, business vs commercial traveling, entertainment, heritage/cultural informative aspects) from comparing UK with Greek GenZers’ responses. These extracted attributes are then presented in two dimensional charts, respectively, toward creating tourist perception scales.

Notwithstanding the wide availability of gamified apps, research on gamification design in tourism and hospitality is still in the early phase. This study demonstrates the need to identify and optimize the formation of different images among GenZers. It also highlights the advantageous nature of the proposed combination of procrustes analysis with the RGA.

To the best of the authors’ knowledge, this research is among the first empirical ones toward creating scales for measuring tourist perceptions of GenZers coming from different consumer markets. It responds to scholars’ recent calls for better informing gamification design and improving contemporary consumer experience.

Although the literature provides insights into the role of experiential learning based on prototypes in identification of latent customer need, it offers different views on the role of product prototypes in improving the efficacy of learning customer need, and also neglects the role of vicarious learning in prototype-based experiential learning. In a data-rich environment, market big data create new opportunities to learn from vicarious, digitalized experiences that are not observable with prototype-based learning. Therefore, the purpose of this study is to compare the effects of product prototype strategies – basic prototype strategy and enhanced prototype strategy – on identification of latent customer needs, and determine how each prototype strategy interacts with vicarious learning based on market big data to identify latent customer needs.

We collected data from 299 Chinese manufacturing firms via on-site surveys to explore our research question. All of our hypotheses were supported by the regression results.

This study finds that both the enhanced and basic prototype strategies (experiential learning from direct market experience based on prototyping) have positive effects on latent need identification, but the effect of enhanced prototypes is stronger. Furthermore, the enhanced and basic prototype strategies have different interaction effects with market big data (vicarious learning from indirect market experiences) on latent need identification.

This research extends the literature on prototype-based learning for latent need identification. It also contributes to the experiential prototype-based learning literature by exploring the role of vicarious learning based on market big data.

The purpose of this paper is to investigate the impact of demand information sharing on products’ greening levels with downstream competition. Specifically, this study examine two types of green products, “development-intensive” (DI) and “marginal-cost intensive” (MI), in a two-echelon supply chain where the manufacturer produces substitutable products, and competing retailers operate in a market with uncertain demand.

The authors adopt the manufacturer-led Stackelberg game-theoretic framework and consider a multistage game. This study consider how retailers receive private signals about uncertain demand and decide whether to share this information with the manufacturer, who then decides whether to acquire this information at a certain given cost. This paper considers backward induction and Bayesian Nash equilibrium to solve the model.

The authors find that in the absence of competition, information sharing is the only equilibrium and improves the greening level under DI, whereas no-information sharing is the only equilibrium and improves the greening level under MI, an increase in downstream competition drives higher investment in greening efforts by the manufacturer in both DI and MI and the manufacturer needs to offer a payment to the retailers to obtain demand information under both simultaneous and sequential contract schemes.

This paper contributes to the literature by examining how the nature of products (margin intensive green product or development intensive green product) influences green supply chain decisions under information asymmetry and downstream competition.

This paper aims to examine the impact of chief executive officer (CEO) attributes on foreign shareholdings from the perspective of an emerging economy.

This study examined Bombay Stock Exchange listed firms from the Indian stock market and applied a balanced panel data approach with fixed effect estimation technique during the period 2010–2019.

The study shows that CEOs’ financial education and a higher level of education positively affect foreign shareholdings. The age and experience of CEO have a positive and significant impact on foreign shareholdings. Firms with male CEOs are preferred more by foreign investors. The effect of CEO busyness and CEO duality is negative on foreign shareholdings. Foreign investors prefer to invest in firms with foreign nationality CEOs. Furthermore, the robustness test reveals that the influence of CEO attributes on foreign shareholdings is stronger for new, small and stand-alone firms than for old, large and group-affiliated firms.

The study will be beneficial for a diverse audience ranging from firms’ board of directors, regulators and policymakers who are entrusted with the CEO recruitment process. Additionally, firms seeking external financing should disclose CEO information adequately and improve the reporting quality to attract foreign investors, as they consider CEO characteristics as a valuable signal before making investment decisions.

In light of the current legislative reforms, this study can be recognized as one of the early studies that explore the relationship between CEO attributes and foreign shareholdings in the context of an emerging economy.

This study aims to examine the factors that influence the increase in purchase intention toward green batik products with religiosity as an intervening variable.

The study used a survey method that involved a sample size of 185 participants selected through purposive and accidental sampling techniques. The analysis was conducted by using IBM SPSS AMOS 21 software. The collected data were subjected to path analysis using multiple linear regression models.

The result indicated that religiosity plays a mediating role in the association between factors and the intention to purchase green product (GPd) of the Indonesian natural dye batik product. This finding is in accordance with the construction of theory of planned behavior in understanding consumer purchase intentions. GPd, green brand and green price exhibited a positive correlation with green purchase intention (GPI). Interestingly, the price was found to no longer serve as the primary factor in GPI.

The analysis would have been more compelling if it had used a mixed-method approach by introducing the variables of customer satisfaction and promotion.

This research postulates that increased prices are no longer a deterrent to the purchase of GPd. Instead, consumer consciousness regarding GPd plays a pivotal role in driving GPI. GPd have revolutionized individuals’ consumption patterns to contribute to environmental preservation. The use of green batik products is seen as advantageous in helping mitigate environmental degradation.

The present research assesses the impact of religiosity, as an intervening variable, on the augmentation of GPI by gauging its significance in enhancing ecological consciousness and moral values.

This paper aims to optimize cable-stayed force in asymmetric one-tower cable-stayed bridge formation using an improved particle swarm algorithm. It compares results with the traditional unconstrained minimum bending energy method.

This paper proposes an improved particle swarm algorithm to optimize cable-stayed force in bridge formation. It formulates a quadratic programming mathematical model considering the sum of bending energies of the main girder and bridge tower as the objective function. Constraints include displacements, stresses, cable-stayed force, and uniformity. The algorithm is applied to optimize the formation of an asymmetrical single-tower cable-stayed bridge, combining it with the finite element method.

The study’s findings reveal significant improvements over the minimum bending energy method. Results show that the structural displacement and internal force are within constraints, the maximum bending moment of the main girder decreases, resulting in smoother linear shape and more even internal force distribution. Additionally, the tower top offset decreases, and the bending moment change at the tower-beam junction is reduced. Moreover, diagonal cable force and cable force increase uniformly with cable length growth.

The improved particle swarm algorithm offers simplicity, effectiveness, and practicality in optimizing bridge-forming cable-staying force. It eliminates the need for arbitrary manual cable adjustments seen in traditional methods and effectively addresses the optimization challenge in asymmetric cable-stayed bridges.

The present work involves analytical and experimental investigation of sloshing in a two-dimensional rectangular tank including the effect of porous baffles to control and/or reduce the wave motion in the sloshing tank. The purpose of this study is to assess the analytical solutions of the drag coefficient effect on porous baffles performance to track free surface motion variation in the sloshing tank by comparison with experimental shake table tests under a range of sway excitation.

The linear second-order ordinary differential equations for liquid sloshing in the rectangular tank were solved using Newmark’s beta method and obtained the analytical solutions for liquid sloshing with dual vertical porous baffles of full submergence depths in a sway-oscillated rectangular tank following the methodology similar to Warnitchai and Pinkaew (1998) and Tait (2008).

The porous baffles significantly reduce wave elevation in the varying filled levels of the tank compared to the baffle-free tank under the range of excitation frequencies. It is observed that the Reynolds number-dependent drag coefficient for porous baffles in the tank can significantly reduce the sloshing elevations and is found to be effective to achieve higher damping compared to the porosity-dependent drag coefficient for porous baffles in the sloshing tank. The analytical model’s response to free surface elevation variations in the sloshing tank was compared with the experiment’s test results. The analytical results matched with shake table test results with a quantitative difference near the first resonant frequency.

The scope of the study is limited to porous baffles performance under range sway motion and three different filling levels in the tank. The porous baffle performance includes Reynolds number dependent drag coefficient to explore the damping effect in the sloshing tank.

The porous baffles with low-level porosities in the sloshing tank have many engineering applications where the first resonant mode of sloshing in the tank is more important. The porous baffle drag coefficient is an important parameter to study the baffle’s damping effect in sloshing tanks. Hence, obtained analytical solution for liquid sloshing in the rectangular tank with Reynolds number as well as porosity-dependent drag coefficient (model 1) and porosity-dependent drag coefficient porous baffles (model 2) performance is discussed. The model’s test results were validated using a series of shake table sloshing experiments for three fill levels in the tank with sway motion at various excitation frequencies covering the first four sloshing resonant modes.

This study aims to analyze and compare the relationship between international trade in global value chains (GVC) and greenhouse gas (GHG) emissions for Brazil and China from 2000 to 2016.

The input-output method apply to multiregional tables from Eora-26 to decompose the GHG emissions of the Brazilian and Chinese productive structure.

The data reveals that Chinese production and consumption emissions are associated with power generation and energy-intensive industries, a significant concern among national and international policymakers. For Brazil, the largest territorial emissions captured by the metrics come from services and traditional industry, which reveals room for improving energy efficiency. The analysis sought to emphasize how the productive structure and dynamics of international trade have repercussions on the environmental dimension, to promote arguments that guide the execution of a more sustainable, productive and commercial development strategy and offer inputs to advance discussions on the attribution of climate responsibility.

The metrics did not capture emissions related to land use and deforestation, which are representative of Brazilian emissions.

Comparative analysis of emissions embodied in traditional sectoral trade flows and GVC, on backward and forward sides, for developing countries with the main economic regions of the world.

Fast obstacle avoidance path planning is a challenging task for multijoint robots navigating through cluttered workspaces. This paper aims to address this issue by proposing an improved path-planning method based on the distorted space (DS) method, specifically designed for high-dimensional complex environments.

The proposed method, termed topology-preserved distorted space (TP-DS) method, mitigates the limitations of the original DS method by preserving space topology through elastic deformation. By applying distinct spring constants, the TP-DS autonomously shrinks obstacles to microscopic areas within the configuration space, maintaining consistent topology. This enhancement extends the application scope of the DS method to handle complex environments effectively.

Comparative analysis demonstrates that the proposed TP-DS method outperforms traditional methods regarding planning efficiency. Successful obstacle avoidance tasks in the cluttered workspace validate its applicability on a physical 6-DOF manipulator, highlighting its potential for industrial implementations.

The novel TP-DS method generates a topology-preserved collision-free space by leveraging elastic deformation and shows significant capability and efficiency in planning obstacle-avoidance paths in complex application scenarios.