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This paper investigates how symmetrical learning activity is, between the public client and the private contractor in the contracting and operation of complex, long-term infrastructure projects. Drawing on empirical material from two United Kingdom (UK) private finance initiative (PFI) cases, the paper analyses differences in the absorptive capacity and learning capability between parties. It suggests the private contractor appears to be better equipped to acquire, embed and renew their learning. These findings reflect less than 5 years of a 30-year contract, suggesting a skewed (imbalanced) relationship, where the contractor gains more learning capabilities than the client. The paper concludes with implications for management practice and suggestions for future research directions.

While the importance of interorganizational learning (IOL) as a resource for strategic renewal has been acknowledged in research, limited attention has been placed on exploring IOL in complex networks or ecosystems. This paper aims to bridge this gap in literature through conceptualizing IOL in the tourism industry at the micro, meso and macro levels of the ecosystem.

This paper applies a narrative literature review to explore the tourism industry and IOL. Through insights which evolved from literature, exploratory typologies and propositions were developed forming the basis of the multi-level typology of interorganizational learning in the tourism industry.

Based on the multi-level typology of interorganizational learning in the tourism industry, market actors may develop in isolation, in clusters or networks, with the aggregate output of these formations constituting the basis of the tourism ecosystem. Within the multi-layers of the tourism ecosystem, IOL may be subdivided into seven distinct typologies, with learning ranging from active, passive, interactive to asymmetric. Each typology of IOL is subsequently influenced by relational ties, which may range from collaborative to competitive, and power distribution, which may lead to symmetric or asymmetric relationships.

The novelty of this paper lies in an ecosystem perspective of IOL in the tourism industry, outlining the multi-level nature of IOL in a destination.

This paper is the first to present empirical evidence consistent with models of signaling through unemployment and to uncover a new stylized fact using the 1988–2006 Displaced Worker Supplement (DWS) of the Current Population Survey (CPS), namely that, among white-collar workers, post-displacement earnings fall less rapidly with unemployment spells for layoffs than for plant closings. Because high-productivity workers are more likely to be recalled than low-productivity ones, they may choose to signal their productivity though unemployment, in which case the duration of unemployment may be positively related to post-displacement wages. Identification is done using workers whose plant closed as they cannot be recalled, and no incentives to signal arise.

This chapter studies the negative signals associated with nonpromotion. I first show theoretically that, when workers' productivity rises little with additional years on the same job level, the negative signal associated with nonpromotion leads to wage decreases. On the other hand, when additional job-level tenure leads to a sizable increase in productivity, workers' wages increase. I then test my model's predictions using the personnel records from a large US firm from 1970–1988. I find a clear hump-shaped wage-job-tenure profile for workers who stay at the same job level, which supports my model's prediction.

This study aims to explore how absorptive and joint learning can foster radical innovation. Furthermore, dependence asymmetry is investigated as a moderator of the effects of these factors on radical innovation. Radical innovation is an important source of any firm’s success. Yet, there has been a dearth of research in the literature on how different types of inter-partner learning cultivate the process of generating such innovation.

The authors use a sample of 204 Taiwanese electronics suppliers to test the effects of joint learning and absorptive learning on radical innovation. The empirical analysis adopts a structural equations modeling approach.

The authors find that a supplier’s joint learning has a stronger effect on radical innovation than its absorptive learning. However, when accounting for the moderating effect of dependence asymmetry, the analysis shows that absorptive learning does have a significant effect on radical innovation. The effect of joint learning on radical innovation is not moderated by the degree of dependence asymmetry.

This study broadens and deepens the understanding of how radical innovation by suppliers can be generated in customer–supplier relationships, and how this is shaped by the power-dependence structure.

Inter-partner learning; radical innovation; power; dependence.

Digital transformation in supply chains (SCs) has emerged as one of the most effective ways to minimize SC disruption risks. Given the unprecedented impact of the COVID-19 pandemic on global SCs, this study aims to identify and provide empirical evidence about the drivers of digital SC transformation, considering the interactivity between environmental dynamism, technology, and organizational capabilities during the pandemic era.

Using partial least squares structural equation modeling (PLS-SEM), this study examines 923 firms in Vietnam to ascertain the drivers of digital SC transformation between small- and medium-sized enterprises (SMEs) and large enterprises, based on the technology–organization–environment (TOE) as an overarching framework.

This study finds that greater digital SC transformation adoption could be achieved under the interactivity between the TOE components of firms' technological competencies, learning capabilities, and disruptions in socioeconomic environments due to the COVID-19 pandemic. Moreover, a multigroup analysis shows that the drivers of digital SC transformation differ between SMEs and large enterprises. SMEs were found to be more motivated by the COVID-19 disruption risk when adopting digital SC models.

This study represents an original and novel contribution from Vietnam as an emerging market to the literature on the impact of COVID-19 on the global value chain. Apart from the unique dataset at the firm level, the analysis of interactions between external and internal drivers of digital SC transformation could provide crucial managerial implications for SMEs to survive major disruptions, such as those caused by the COVID-19 pandemic.

This chapter develops an on-going theory of coopetition management in knowledge-based industries. Coopetition is a strategy which combines simultaneously competitive and collaborative relationships. This combination permits companies to benefit from both the advantages of the competition and the advantage of collaboration. However, this strategy is also risky in case of unintended spillovers and technology plunders. Companies have to manage the coopetitive risk by implementing three principles of coopetition management: the separation principle, the integration principle, and the co-management principle.

This chapter highlights and examines the journey of strategic alliances, how alliances lead to the creation of new alliances, and whether alliances succeed or precede firms' market networks. Moreover, the chapter attempts to identify the market and environmental factors which impact the journey and the end result of alliances. The findings show that before the alliance and in the early strategic alliance phase, firms can have direct and indirect relationships/networks which can lead to the formation of strategic alliances. The other finding is that one of the main factors which determines the journey of strategic alliances is the degree of internationalization of the firm and the market. The journey of alliances was also found to be different in the various groups of Eastern and Central Europe (ECE) countries depending on the pace or degree of adaptation of those countries. It was also found that well-developed networks have a positive impact on the alliance results. Moreover, the chapter also provides evidence that alliances enable firms to defend and strengthen existing networks to build new networks and to penetrate partners' networks. It is finally observed that it is difficult to determine whether alliances succeed or precede networks.

Promoting “Quality Education” as part of Sustainable Development Goal 4 requires educational stakeholders to understand the nature of online learning from the learners’ perspective. This study aims to explore the asymmetric effects of online learning attributes on overall learning satisfaction in tourism and hospitality education.

The authors adopt the three-factor satisfaction theory and the three analytical steps penalty–reward contrast analysis, impact range performance analysis (IRPA) and impact asymmetry analysis. The authors then juxtapose the results of such analytical approaches to the traditional importance–performance analysis (IPA) for further insights.

Traditional IPA suggests nine high importance and performance online attributes. However, the IRPA reveals that only four attributes fall within high performance and high range of impact on satisfaction. The attribute “secure” had a low performance despite a significant impact on satisfaction.

By exploring asymmetric effects, scholars, service providers and participants of online education can have a deeper insight into the nature and dynamic effect of attributes on overall satisfaction.

By exploring the asymmetric impacts of online learning attributes on overall learning satisfaction, the findings provide a novel insight into the multidimensionality of online learning attributes.

推动“优质教育”作为可持续发展目标四的一部分, 要求教育利益相关者从学习者的角度从本质上了解在线学习。本研究探讨了在线学习属性对旅游和酒店教育整体学习满意度的非对称影响。

我们采用三因素满意度理论结合三个分析步骤：惩罚奖励对比分析（PRCA）; 影响范围性能分析（IRPA）; 影响非对称分析（IAA）, 并将这种分析方法的结果与传统的重要性-表现分析（IPA）对比以获得深入理解。

传统的重要性-表现分析的结果展现了在线学习满意度具有较高重要性和表现的九个属性。然而, IRPA 显示只有四个属性属于高性能和满意度的高影响范围。“安全”这一属性虽然对满意度有显着影响, 但它的表现评估却很低。

对非对称影响的探索帮助在线教育的学者、服务提供者和参与者们更深入地了解对于整体满意度而言不同属性的本质和动态影响。

通过探讨在线学习属性对整体学习满意度的非对称影响, 我们的研究结果为理解在线学习属性对于整体满意度的影响提供了多维度的新颖视角。

La promoción de la “Educación de Calidad” como parte del Objetivo de Desarrollo Sostenible 4 requiere que los actores educativos entiendan la naturaleza del aprendizaje en línea desde la perspectiva de los estudiantes. Este estudio explora los efectos asimétricos de los atributos del aprendizaje en línea sobre la satisfacción general del aprendizaje en la educación en turismo y hotelería.

Aplicamos la teoría de la satisfacción de tres factores y los tres pasos analíticos: análisis de contraste de penalización-recompensa (PRCA), análisis de rendimiento del rango de impacto (IRPA) y análisis de impacto asimétrico (IAA). A continuación, aplicamos los resultados de estos enfoques analíticos al tradicional análisis de importancia-rendimiento (IPA) para obtener más información.

El análisis tradicional de importancia-rendimiento sugiere nueve atributos en línea de gran importancia y rendimiento. Sin embargo, el IRPA revela que sólo cuatro atributos se encuentran dentro de la franja de alto rendimiento y alto impacto en la satisfacción. El atributo “seguridad” tuvo un desempeño bajo a pesar de tener un impacto significativo en la satisfacción.

Al explorar los efectos asimétricos, los académicos, los proveedores de servicios y los participantes de la educación en línea pueden tener una visión más profunda de la naturaleza y el efecto dinámico de los atributos en la satisfacción general.

Al explorar los impactos asimétricos de los atributos del aprendizaje en línea en la satisfacción general del aprendizaje, nuestros hallazgos brindan una visión novedosa de la multidimensionalidad de los atributos del aprendizaje en línea.