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In this paper we study the association between Chief Executive Officers’ strategic human resource capital, dynamic managerial capabilities, and firms’ dynamic capabilities. This study identifies the Chief Executive Officer (CEO) variables reported in the literature, and estimates the correlation of each CEO variable with the dynamic capabilities. In addition, the moderator effect of the three types of dynamic capabilities is estimated. This paper applies a meta-analysis using structural equation modeling by means of the three-level random-effects model on 446 correlations, which account for 52,767 CEOs around the world. The findings show a positive correlation of CEO education (ρ = .113), CEO personality (ρ = .162), and leadership styles (ρ = .306) with dynamic capabilities. In addition, dynamic managerial capabilities totally mediate the relationship between education and dynamic capabilities and partially the relationship of CEO personality and leadership styles with dynamic capabilities.

The connection between the policies and practices of human resource management of total quality management and technological innovation in Spanish industrial firms is studied. Results reveal a direct and possible moderating influence, of strategic orientation on technological innovation.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.technovation.2005.09.008 Byline: Jesus Perdomo-Ortiz, Javier Gonzalez-Benito, Jesus Galende Keywords: Quality; Total quality management; Innovation; Business innovation capability; Measurement scales Abstract: Although it is true that the issues of quality and innovation have been dealt with extensively from different perspectives, studies on the relationship between the two concepts are few. This study tackles this relationship by analyzing the links between the broader concepts of total quality management (TQM) and business innovation capability (BIC). Specifically, it is argued that TQM can favor the development of BIC. An empirical study of 102 firms in the sectors of machinery and instruments for measurement, analysis and control revealed that, indeed, both concepts are compatible and make it possible to identify which dimensions of TQM explain the generation of BIC. Author Affiliation: Department of Business Administration and Management, University of Salamanca, Spain

Purpose: The purpose of this paper is to find evidence of construct validity in a Mexican population for a socially responsible consumption (SRC) measurement scale originally proposed for Colombia. Design/methodology/approach: Using a quantitative approach and a correlational scope, factorial invariance and differential item functioning analyses were performed on the SRC measurement scale based on data from 323 Colombian graduate students and 456 Mexican students. Findings: The empirical evidence confirms that the factor structure of the SRC measurement scale applied in Colombia remains valid in the Mexican context. Research limitations/implications: Limitations pertain to the attitudinal measurement scale and the non-representativeness of the chosen samples. Practical implications: When identifying a structure for SRC, particularly considering the formulation of marketing strategies and business reputation, companies should consider the new criteria of buying consumption. Originality/value: For the first time in Latin America, an SRC measurement scale is constructed, and its construct validity is evaluated for two countries: Mexico and Colombia. The authors thus provide empirical evidence for the hypothesis that SRC measurement is contingent on the expression of measurement considering the socioeconomic and cultural context in which the study is conducted. Propósito: Buscar evidencia de validez de constructo en una población mexicana para la escala de medida del Consumo Socialmente Responsable (CSR) propuesta para Colombia. Diseño/metodología/enfoque: Con un enfoque cuantitativo y alcance correlacional se realiza un análisis de invarianza factorial y de funcionamiento diferencial del ítem (DIF), de la escala de medida del CSR en 323 estudiantes de posgrado colombianos y 456 mexicanos. Hallazgos: La evidencia empírica confirma que la estructura factorial de la escala de medida del CSR aplicado en Colombia se mantiene en el contexto mexicano. Limitaciones de la investigación/implicaciones: Las propias de las escalas de medida actitudinal y de la no representatividad de las muestras elegidas. Implicaciones prácticas: Cuando se identifica una estructura de un CSR, las empresas en la formulación de estrategias de mercadeo y reputación empresarial, deben considerar los nuevos criterios de compra de consumo. Originalidad/valor: Por primera vez en Latinoamérica se construye una escala de medida del CSR y se realiza una validez de constructo considerando dos países como lo son México y Colombia. En este sentido, se aporta evidencia empírica a la hipótesis según la cual la medición del CSR es contingente en su expresión de medida al contexto socioeconómico y cultural donde se realice el estudio.

We introduce a method for the problem of learning the structure of a Bayesian network using the quantum adiabatic algorithm. We do so by introducing an efficient reformulation of a standard posterior-probability scoring function on graphs as a pseudo-Boolean function, which is equivalent to a system of 2-body Ising spins, as well as suitable penalty terms for enforcing the constraints necessary for the reformulation; our proposed method requires 𝓞(n2)qubits for nBayesian network variables. Furthermore, we prove lower bounds on the necessary weighting of these penalty terms. The logical structure resulting from the mapping has the appealing property that it is instance-independent for a given number of Bayesian network variables, as well as being independent of the number of data cases.

Diagnosing the minimal set of faults capable of explaining a set of given observations, e.g., from sensor readouts, is a hard combinatorial optimization problem usually tackled with artificial intelligence techniques. We present the mapping of this combinatorial problem to quadratic unconstrained binary optimization (QUBO), and the experimental results of instances embedded onto a quantum annealing device with 509 quantum bits. Besides being the first time a quantum approach has been proposed for problems in the advanced diagnostics community, to the best of our knowledge this work is also the first research utilizing the route Problem → QUBO → Direct embedding into quantum hardware, where we are able to implement and tackle problem instances with sizes that go beyond previously reported toy-model proof-of-principle quantum annealing implementations; this is a significant leap in the solution of problems via direct-embedding adiabatic quantum optimization. We discuss some of the programmability challenges in the current generation of the quantum device as well as a few possible ways to extend this work to more complex arbitrary network graphs.

We introduce a method for the problem of learning the structure of a Bayesian network using the quantum adiabatic algorithm. We do so by introducing an efficient reformulation of a standard posterior-probability scoring function on graphs as a pseudo-Boolean function, which is equivalent to a system of 2-body Ising spins, as well as suitable penalty terms for enforcing the constraints necessary for the reformulation; our proposed method requires 𝓞(n2) qubits for nBayesian network variables. Furthermore, we prove lower bounds on the necessary weighting of these penalty terms. The logical structure resulting from the mapping has the appealing property that it is instance-independent for a given number of Bayesian network variables, as well as being independent of the number of data cases.

Although the performance of hybrid quantum-classical algorithms is highly dependent on the selection of the classical optimizer and the circuit ansätze (Benedetti et al, npj Quantum Inf 5:45, 2019; Hamilton et al, 2018; Zhu et al, 2018), a robust and thorough assessment on-hardwareof such features has been missing to date. From the optimizer perspective, the primary challenge lies in the solver’s stochastic nature, and their significant variance over the random initialization. Therefore, a robust comparison requires one to perform several training curves for each solver before one can reach conclusions about their typical performance. Since each of the training curves requires the execution of thousands of quantum circuits in the quantum computer, such a robust study remained a steep challenge for most hybrid platforms available today. Here, we leverage on Rigetti’s Quantum Cloud Services (QCS™) to overcome this implementation barrier, and we study the on-hardwareperformance of the data-driven quantum circuit learning (DDQCL) for three different state-of-the-art classical solvers, and on two-different circuit ansätze associated to different entangling connectivity graphs for the same task. Additionally, we assess the gains in performance from varying circuit depths. To evaluate the typical performance associated with each of these settings in this benchmark study, we use at least five independent runs of DDQCL towards the generation of quantum generative models capable of capturing the patterns of the canonical Bars and Stripes dataset. In this experimental benchmarking, the gradient-free optimization algorithms show an outstanding performance compared to the gradient-based solver. In particular, one of them had better performance when handling the unavoidable noisy objective function to be minimized under experimental conditions.

To date, a conclusive detection of quantum speedup remains elusive. Recently, a team by Google Inc. [V. S. Denchev et al., Phys. Rev. X 6, 031015 (2016)] proposed a weak-strong cluster model tailored to have tall and narrow energy barriers separating local minima, with the aim to highlight the value of finite-range tunneling. More precisely, results from quantum Monte Carlo simulations as well as the D-Wave 2X quantum annealer scale considerably better than state-of-the-art simulated annealing simulations. Moreover, the D-Wave 2X quantum annealer is ~108 times faster than simulated annealing on conventional computer hardware for problems with approximately 10³ variables. Here, an overview of different sequential, nontailored, as well as specialized tailored algorithms on the Google instances is given. We show that the quantum speedup is limited to sequential approaches and study the typical complexity of the benchmark problems using insights from the study of spin glasses. [ABSTRACT FROM AUTHOR]

An increase in the efficiency of sampling from Boltzmann distributions would have a significant impact on deep learning and other machine-learning applications. Recently, quantum annealers have been proposed as a potential candidate to speed up this task, but several limitations still bar these state-of-the-art technologies from being used effectively. One of the main limitations is that, while the device may indeed sample from a Boltzmann-like distribution, quantum dynamical arguments suggest it will do so with an instance-dependent effective temperature, different from its physical temperature. Unless this unknown temperature can be unveiled, it might not be possible to effectively use a quantum annealer for Boltzmann sampling. In this work, we propose a strategy to overcome this challenge with a simple effective-temperature estimation algorithm. We provide a systematic study assessing the impact of the effective temperatures in the learning of a special class of a restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep-learning architectures. We also provide a comparison to k-step contrastive divergence (CD-k) with k up to 100. Although assuming a suitable fixed effective temperature also allows us to outperform one-step contrastive divergence (CD-1), only when using an instance-dependent effective temperature do we find a performance close to that of CD-100 for the case studied here. [ABSTRACT FROM AUTHOR]