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This study identifies three emerging approaches for leading successful digital transformation. It challenges the validity and effectiveness of traditional linear processes for digital transformation that first develop a new strategy, business model or organizational design enabled by digital technologies and then execute it according to the plan.

The paper is based on current research with a group of global digital champions at the forefront of digital transformation, including Amazon, Alibaba, Baidu, Google, JD.com, Uber, VMWare and Slack.

The research finds that at least three new approaches are emerging in leading organizations, which are (1) innovating by experimenting, (2) radical transformation via successive incremental changes and (3) dynamic sustainable advantages through an evolving portfolio of temporary advantages.

More research is needed to systematically investigate such emerging approaches and develop new theories and new analytical frameworks.

These emerging approaches can be used to guide digital transformation initiatives.

They also enable business leaders to tackle grand societal challenges using financially sustainable and scalable operational models, generating profit and impact at the same time.

It challenges traditional linear approaches for leading digital transformation and highlights the need for new and iterative approaches for bridging the strategy–execution gap in the volatile digital economy.

This paper aims to present a multi-scale stochastic damage model (SDM) for concrete and apply it to the stochastic response analysis of reinforced concrete shear wall structures.

The proposed SDM is constructed at two scales, i.e. the macro-scale and the micro-scale. The general framework of the SDM is established on the basis of the continuum damage mechanics (CDM) at the macro-scale, whereas the detailed damage evolution is determined through a parallel fiber buddle model at the micro-scale. The parallel buddle model is made up of micro-elements with stochastic fracture strains, and a one-dimensional random field is assumed for the fracture strain distribution. To represent the random field, a random functional method is adopted to quantify the stochastic damage evolution process with only two variables; thus, the numerical efficiency is greatly enhanced. Meanwhile, the probability density evolution method (PDEM) is introduced for the structural stochastic response analysis.

By combing the SDM and PDEM, the probabilistic analysis of a shear wall structure is performed. The mean value, standard deviation and the probability density function of the shear wall responses, e.g., shear capacity, accumulated energy consumption and damage evolution, are obtained.

It is noted that the proposed method can reflect the influences of randomness from material level to structural level, and is efficient for stochastic response determination of shear wall structures.

The application of the traditional failure mode and effects analysis (FMEA) technique has been widely questioned in evaluation information, risk factor weights and robustness of results. This paper develops a novel FMEA framework with extended MULTIMOORA method under interval-valued Pythagorean fuzzy environment to solve these problems.

This paper introduces innovatively interval-value Pythagorean fuzzy weighted averaging (IVPFWA) operator, Tchebycheff metric distance and interval-value Pythagorean fuzzy weighted geometric (IVPFWG) operator into the MULTIMOORA submethods to obtain the risk ranking order for emergencies. Finally, an illustrative case is provided to demonstrate the practicality and feasibility of the novel fuzzy FMEA framework.

The feasibility and validity of the proposed method are verified by comparing with the existing methods. The calculation results indicate that the proposed method is more consistent with the actual situation of project and has more reference value.

The research results can provide supporting information for risk management decisions and offer decision-making basis for formulation of the follow-up emergency control and disposal scheme, which has certain guiding significance for the practical popularization and application of risk management strategies in the infrastructure projects.

A novel approach using FMEA with extended MULTIMOORA method is developed under IVPF environment, which considers weights of risk factors and experts. The method proposed has significantly improved the integrity of information in expert evaluation and the robustness of results.

This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971–2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed- and random coefficients model to estimate this relationship. In particular, this chapter uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), the authors estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.

Trust is the glue that holds cooperative relationships together and often exists in an asymmetric manner. The purpose of this study is to explore how to mitigate the issue of losses or increased transaction costs caused by opportunistic behavior in a soft environment where trust asymmetry is quite common and difficult to avoid.

This study focuses on examining asymmetric trust between the government and the private sector in public-private partnership (PPP) projects. Drawing upon both project realities and relevant literature, the primary conditional variables influencing asymmetric trust are identified. These variables encompass power perception asymmetry, information asymmetry, interaction behavior, risk perception differences and government-side control. Subsequently, through the use of a survey questionnaire, binary-matched data from both the government and the private sector are collected. The study employs fuzzy-set qualitative comparative analysis (fsQCA) to conduct a configurational analysis, aiming to investigate the causal pathways that trigger asymmetric trust.

No single conditional variable is a necessary condition for the emergence of trust asymmetry. The pathways leading to a high degree of trust asymmetry can be categorized into two types: those dominated by power perception and those involving a combination of multiple factors. Differences in power perception play a crucial role in the occurrence of high trust asymmetry, yet the influence of other conditional variables in triggering trust asymmetry should not be overlooked.

The findings can contribute to advancing the study of trust relationships in the field of Chinese PPP projects. Furthermore, they hold practical value in facilitating the enhancement of trust relationships between the government and the private sector.

The purpose of this paper is to propose a novel jump control method based on Two Mass Spring Damp Inverted Pendulum (TMS-DIP) model, which makes the third generation of hydraulic driven wheel-legged robot prototype (WLR-3P) achieve stable jumping.

First, according to the configuration of the WLR, a TMS-DIP model is proposed to simplify the dynamic model of the robot. Then the jumping process is divided into four stages: thrust, ascent, descent and compression, and each stage is modeled and solved independently based on TMS-DIP model. Through WLR-3P kinematics, the trajectory of the upper and lower centroids of the TMS-DIP model can be mapped to the joint space of the robot. The corresponding control strategies are proposed for jumping height, landing buffer, jumping attitude and robotic balance, so as to realize the stable jump control of the WLR.

The TMS-DIP model proposed in this paper can simplify the WLR dynamic model and provide a simple and effective tool for the jumping trajectory planning of the robot. The proposed approach is suitable for hydraulic WLR jumping control. The performance of the proposed wheel-legged jump method was verified by experiments on WLR-3P.

This work provides an effective model (TMS-DIP) for the jump control of WLR-3P. The results showed that the number of landing shock (twice) and the pitch angle fluctuation range (0.44 rad) of center of mass of the jump control method based on TMS-DIP model are smaller than those based on spring-loaded inverted pendulum model. Therefore, the TMS-DIP model makes the jumping process of WLR more stable and gentler.

The purpose of this paper is to investigate the decomposition behavior of binary mixtures of organic activators commonly used in the no-clean wave flux systems upon their exposure to thermal treatments simulating wave soldering temperatures. The binary blends of activators were studied at varying ratios between the components.

Differential scanning calorimetry and thermogravimetric analysis were used to study the characteristics of weak organic acid (WOA) mixtures degradation as a function of temperature. The amount of residue left on the surface after the heat treatments was estimated by gravimetric measurements as a function of binary mixture type, temperature and exposure time. Ion chromatography analysis was used for understanding the relative difference between decomposition of activators in binary blends. The aggressivity of the left residue was assessed using the acidity indication gel test, and effect on reliability was investigated by DC leakage current measurement performed under varying humidity and potential bias conditions.

The results show that the typical range of temperatures experienced by electronics during the wave soldering process is not sufficient for the removal of significant activator amounts. If the residues contain binary mixture of WOAs, the final ratio between the components, the residue level and the corrosive effects depend on the relative decomposition behavior of individual components. Among the WOA investigated under the conventional wave soldering temperature, the evaporation and removal of succinic acid is more dominant compared to adipic and glutaric acids.

The findings are attributed to the chemistry of WOAs typically used as flux activators for wave soldering purposes. The results show the importance of controlling the WOA content and ratio between activating components in a flux formulation in relation to its tendencies for evaporation during soldering and the impact of its residues on electronics reliability.

The results show that the significant levels of flux residues can only be removed at significantly higher temperatures and longer exposure times compared to the conventional temperature range used for the wave soldering process. The potential corrosion issues related to insufficient flux residues removal will be determined by the residue amount, its composition and ratio between organic components. The proper time of thermal treatment and careful choice of fluxing formulation could ensure more climatically reliable product.

The promotion of new energy vehicles (EVs) is an effective way to achieve low carbon emission reduction. This paper aims to investigate the optimal pricing of automotive supply chain members in the context of dual policy implementation while considering consumers' low-carbon preferences.

This article takes manufacturers, retailers and consumers in a main three-level supply chain as the research object. Stackelberg game theory is used as the theoretical guidance. A game model in which the manufacturer is the leader and the retailer is the follower is established. The author also considered the impact of carbon tax policies, subsidy policies and consumer preferences on the results. Furthermore, the author investigates the optimal decision-making problem under the profit maximization model.

Through model solving, it is found that the pricing of EVs is positively correlated with the unit price of carbon and the amount of subsidies. The following conclusions can be obtained by numerical analysis of each parameter. Changes in carbon prices have a greater impact on conventional gasoline vehicles. Based on the numerical analysis of parameter β, it is also found that when the government subsidizes consumers, supply chain members will increase their prices to obtain partial subsidies. Compared with retailers, low-carbon preferences have a greater impact on manufacturers.

The new energy automobile industry involves many policies, including tax cuts, tax exemptions and subsidies. The policy environment faced by the members of a supply chain is complex and diverse. Therefore, the analysis in this article is based only on partial policies.

The authors innovatively combine the three factors of subsidy policy, carbon tax policy and consumer low-carbon preference, with research on the pricing of EVs. The influence of policy factors and consumer preferences on the pricing of EVs is studied.

Merger and acquisitions (M&A) is a process of restructuring two or more companies into one, a process that occurs frequently in many companies. Previous studies on M&A mainly paid attention to the potential gains from a merger, while ignored the problem of how to select the partners to merge. This paper aims to select the best partner from different candidates for a given company to merge.

Each company's historical data are used to identify each company's own production technology. With resources change, each company's new operation is restricted by its own production technology. Then, a 0–1 integer programming is proposed to select the best partner for M&A.

The banking industry involving 27 China's commercial banks is given to verify the applicability of our proposed model. The study shows the best partner selection for each bank company.

On the theoretical side, the study uses each company's own historical data to construct its own production technology to compressively reflect the production change after M&A. On the practical side, the study uses the proposed model to help the 27 commercial banks in China to select their best merger partner.

From a relational maintenance perspective, this study explores the impact of being envied (benignly vs maliciously) on consumers' feelings of social anxiety and its influence on their tendencies toward inconspicuous consumption, based on the resource conservation theory and the model of “Sensitivity about Being the Target of a Threatening Upward Comparison.” (STTUC)

Four studies were conducted in this paper. Studies 1a and 1b tested the main hypothesis that being maliciously envied (vs benignly) can increase consumers' inconspicuous consumption of luxury products and luxury hotel experiences. Study 2 replicated this finding and examined the mediating role of social anxiety. Study 3 investigated the moderating effect of ideal self-congruity (low vs high).

The findings reveal that being maliciously envied (vs benignly) is associated with higher levels of inconspicuous consumption and social anxiety acts as a mediating role. Moreover, when individuals have a strong sense of ideal self-congruity, the positive impact of being maliciously envied (vs benignly) on inconspicuous consumption is further amplified, confirming the moderating role of ideal self-congruity.

This study sheds light on a novel mechanism that elucidates how different types of being envied influence consumers' inconspicuous consumption and the conditions under which this impact is heightened.