Charlotte Reypens and Sheen S. Levine
Measuring behavior requires research methods that can capture observed outcomes and expose underlying processes and mechanisms. In this chapter, we present a toolbox of…
Abstract
Measuring behavior requires research methods that can capture observed outcomes and expose underlying processes and mechanisms. In this chapter, we present a toolbox of instruments and techniques we designed experimental tasks to simulate decision environments and capture behavior. We deployed protocol analysis and text analysis to examine the underlying cognitive processes. In combination, these can simultaneously grasp antecedents, outcomes, processes, and mechanisms. We applied them to collect rich behavioral data on two key topics in strategic management: the exploration–exploitation trade-off and strategic risk-taking. This mix of methods is particularly useful in describing actual behavior as it is, not as it should be, replacing assumptions with data and offering a finer-grained perspective of strategic decision-making.
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Chengwei Liu and Chia-Jung Tsay
Chance models – mechanisms that explain empirical regularities through unsystematic variance – have a long tradition in the sciences but have been historically marginalized in…
Abstract
Chance models – mechanisms that explain empirical regularities through unsystematic variance – have a long tradition in the sciences but have been historically marginalized in management scholarship, relative to an agentic worldview about the role of managers and organizations. An exception is the work of James G. March and his coauthors, who proposed a variety of chance models that explain important management phenomena, including the careers of top executives, managerial risk taking, and organizational anarchy, learning, and adaptation. This paper serves as a tribute to the beauty of these “little ideas” and demonstrates how they can be recombined to generate novel implications. In particular, we focus on the example of an inverted V-shaped performance association centering around the year when executives were featured in a prominent listing, Barron’s annual list of Top 30 chief executive officers. Our recombination of several chance models developed by March and his coauthors provides a novel explanation for why many of the executives’ exceptional performances did not persist. In contrast to the common accounts of complacency, hubris, and statistical regression, the results show that declines from high performance may result from the way luck interacts with these executives’ slow adaptation, incompetence, and self-reinforced risk taking. We conclude by elaborating on the normative implications of chance models, which address many current management and societal challenges. We further encourage the continued development of chance models to help explain performance differences, shifting from accounts that favor heroic stories of corporate leaders toward accounts that favor their changing fortunes.
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Unnati Narang and Venkatesh Shankar
Mobile marketing, the two- or multi-way communication and promotion of an offer between a firm and its customers using a mobile medium, device, platform, or technology, has made…
Abstract
Mobile marketing, the two- or multi-way communication and promotion of an offer between a firm and its customers using a mobile medium, device, platform, or technology, has made rapid strides in the past several years. Mobile marketing has entered its second phase or Mobile Marketing 2.0. The surpassing of desktop by mobile devices in digital media consumption, diffusion of wearable devices among customers, and an overall integration and interconnectedness of devices characterize this phase. Against this backdrop, we present a synthesis of the most recent literature in mobile marketing. We discuss three key advances in mobile marketing research relating to mobile targeting, personalization, and mobile-led cross-channel effects. We outline emerging industry trends in mobile marketing, including mobile app monetization, augmented reality, data and privacy, wearable devices, driverless vehicles, the Internet of Things, and artificial intelligence. Within each extant and emerging area, we delineate the future research opportunities in mobile marketing. Finally, we discuss the impact of mobile marketing on customer, firm, and societal outcomes.
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Jiali Fang, Yining Tian and Yuanyuan Hu
The purpose of this study is to examine the relationship between the corporate social responsibility (CSR) performance of job-hopping executives at their former and subsequent…
Abstract
Purpose
The purpose of this study is to examine the relationship between the corporate social responsibility (CSR) performance of job-hopping executives at their former and subsequent firms.
Design/methodology/approach
We conduct regression analyses using a sample of firms listed on the Shanghai and Shenzhen Stock Exchanges from 2010 to 2020 to examine whether CSR performance is similar from one firm to the next as executives switch jobs.
Findings
We find a positive relationship between the CSR performance of former and subsequent firms under job-hopping executives. This relationship is the strongest in the year of the job switch; it weakens in the second year and eventually disappears in the third year. In addition, we show that this relationship benefits different CSR stakeholder groups and is contingent on executive and subsequent firm attributes and job-hopping characteristics. Furthermore, we demonstrate that firms that hire a new chief executive officer from a firm with a strong track record in CSR, the new firm experiences a significant surge in CSR performance compared with firms that do not experience such a shock.
Practical implications
This study has implications for executive hiring decisions.
Originality/value
This study extends the understanding of CSR determinants through the lens of inter-organisational ties associated with job-hopping executives.
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Christina Fang and Chengwei Liu
Behavioral strategy completes the analyses of superior profitability by highlighting how non-economic, behavioral barriers generate an alternative source of strategic…
Abstract
Behavioral strategy completes the analyses of superior profitability by highlighting how non-economic, behavioral barriers generate an alternative source of strategic opportunities. Existing internal and external analysis frameworks fail to explain why strategic factors can be systematically mispriced and why large firms’ structural and resource advantage are regularly disrupted by entrepreneurs. We argue that the systematic biases documented in the behavioral and organizational sciences in fact illuminate an alternative source of competitive advantage. Strategists could develop superior insights into the value of resources and recognize factors that are either under- or overvalued while competitors remain blind to such possibilities. Our argument is illustrated by how three “underdogs” disrupted the incumbents in their industries by exploiting rivals’ predictable biases and blind spots. We conclude by discussing how our ideas can be generalized as an alternative, behavioral approach for strategy.
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Mostafa Abbaszadeh, AliReza Bagheri Salec and Shurooq Kamel Abd Al-Khafaji
The space fractional PDEs (SFPDEs) play an important role in the fractional calculus field. Proposing a high-order, stable and flexible numerical procedure for solving SFPDEs is…
Abstract
Purpose
The space fractional PDEs (SFPDEs) play an important role in the fractional calculus field. Proposing a high-order, stable and flexible numerical procedure for solving SFPDEs is the main aim of most researchers. This paper devotes to developing a novel spectral algorithm to solve the FitzHugh–Nagumo models with space fractional derivatives.
Design/methodology/approach
The fractional derivative is defined based upon the Riesz derivative. First, a second-order finite difference formulation is used to approximate the time derivative. Then, the Jacobi spectral collocation method is employed to discrete the spatial variables. On the other hand, authors assume that the approximate solution is a linear combination of special polynomials which are obtained from the Jacobi polynomials, and also there exists Riesz fractional derivative based on the Jacobi polynomials. Also, a reduced order plan, such as proper orthogonal decomposition (POD) method, has been utilized.
Findings
A fast high-order numerical method to decrease the elapsed CPU time has been constructed for solving systems of space fractional PDEs.
Originality/value
The spectral collocation method is combined with the POD idea to solve the system of space-fractional PDEs. The numerical results are acceptable and efficient for the main mathematical model.
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Zheng Xu, Yihai Fang, Nan Zheng and Hai L. Vu
With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.
Abstract
Purpose
With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.
Design/methodology/approach
The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.
Findings
Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.
Research limitations/implications
Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.
Practical implications
This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.
Social implications
This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.
Originality/value
A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.
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Hangjun Zhang, Jinhui Fang, Jianhua Wei, Huan Yu and Qiang Zhang
This paper aims to present an adaptive sliding mode control (ASMC) for tunnel boring machine cutterhead telescopic system with uncertainties to achieve a high-precision trajectory…
Abstract
Purpose
This paper aims to present an adaptive sliding mode control (ASMC) for tunnel boring machine cutterhead telescopic system with uncertainties to achieve a high-precision trajectory in complex strata. This method could be applied to solve the problems caused by linear and nonlinear model uncertainties.
Design/methodology/approach
First, an integral-type sliding surface is defined to reduce the static tracking error. Second, a projection type adaptation law is designed to approximate the linear and nonlinear redefined parameters of the electrohydraulic system. Third, a nonlinear robust term with a continuous approximation function is presented for handling load force uncertainty and reducing sliding mode chattering. Moreover, Lyapunov theory is applied to guarantee the stability of the closed-loop system. Finally, the effectiveness of the proposed controller is proved by comparative experiments on a scaled test rig.
Findings
The linear and nonlinear model uncertainties lead to large variations in the dynamics of the mechanism and the tracking error. To achieve precise position tracking, an adaptation law was integrated into the sliding mode control which compensated for model uncertainties. Besides, the inherent sliding mode chattering was reduced by a continuous approximation function, while load force uncertainty was solved by a nonlinear robust feedback. Therefore, a novel ASMC for tunnel boring machine cutterhead telescopic system with uncertainties can improve its tracking precision and reduce the sliding mode chattering.
Originality/value
To the best of the authors’ knowledge, the ASMC is proposed for the first time to control the tunnel boring machine cutterhead telescopic system with uncertainties. The presented control is effective not only in control accuracy but also in parameter uncertainty.
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Bhumeshwar Kujilal Patle, Shyh-Leh Chen, Anil Singh and Sunil Kumar Kashyap
The paper aims to develop an efficient and compact hybrid S-curve-PSO (particle swarm optimization) controller for the optimal trajectory planning of industrial robots in the…
Abstract
Purpose
The paper aims to develop an efficient and compact hybrid S-curve-PSO (particle swarm optimization) controller for the optimal trajectory planning of industrial robots in the presence of obstacles, especially those used in pick-and-place operations.
Design/methodology/approach
The proposed methodology comprises a monotonic trajectory through bounded entropy of speed, velocity, acceleration and jerk. Thus, the robot’s trajectory planning corresponds with S-curve-PSO duality. This is achieved by dual navigation with minimal computational complexity. The matrix algebra-based computational complexity transforms the trajectory from random to compact. The linear programming problem represents the proposed robot in Euclidean space, and its optimal solution sets the corresponding optimal trajectory.
Findings
The proposed work ensures the efficient trajectory planning of the industrial robot in the presence of obstacles with optimized path length and time. The real-time and simulation analysis of the robot is presented for performance measurement, and their outcomes demonstrate a good correlation. Compared with the existing controller, it gives a noteworthy improvement in performance.
Originality/value
The novel S-curve-PSO hybrid approach is presented here, along with the LIDAR sensors, which generate the environment map and detect obstacles for autonomous trajectory planning. Based on the sensory information, the proposed approach generates the optimal trajectory by avoiding obstacles and minimizing the travel time, jerk, velocity and acceleration. The hybrid S-curve-PSO approach for optimal trajectory planning of the industrial robot in the presence of obstacles has not been presented by any researchers. This method considers the robot’s kinematics as well as its dynamics. The implementation of the PSO makes it computationally superior and faster. The selection of best-fit parameters by PSO assures the optimized trajectory in the presence of obstacles and uncertainty.