“It’s Like Hating Puppies!” Employee Disengagement and Corporate Social Responsibility

Corporate social responsibility (CSR) has been linked with numerous organizational advantages, including recruitment, retention, productivity, and morale, which relate specifically to employees. However, despite specific benefits of CSR relating to employees and their importance as a stakeholder group, it is noteworthy that a lack of attention has been paid to the individual level of analysis with CSR primarily being studied at the organizational level. Both research and practice of CSR have largely treated the individual organization as a “black box,” failing to account for individual differences amongst employees and the resulting variations in antecedents to CSR engagement or disengagement. This is further exacerbated by the tendency in stakeholder theory to homogenize priorities within a single stakeholder group. In response, utilizing case study data drawn from three multinational tourism and hospitality organizations, combined with extensive interview data collected from CSR leaders, industry professionals, engaged, and disengaged employees, this exploratory research produces a finer-grained understanding of employees as a stakeholder group, identifying a number of opportunities and barriers for individual employee engagement in CSR interventions. This research proposes that employees are situated along a spectrum of engagement from actively engaged to actively disengaged. While there are some common drivers of engagement across the entire spectrum of employees, differences also exist depending on the degree to which employees, rather than senior management, support corporate responsibility within their organizations. Key antecedents to CSR engagement that vary depending on employees’ existing level of broader engagement include organizational culture, CSR intervention design, employee CSR perceptions, and the observed benefits of participation

Unusual devitrification behaviour in rapidly solidified Ti45-Zr38-Ni17 alloy

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Semantic Deep Mapping in the Amsterdam Time Machine:Viewing Late 19th- and Early 20th-Century Theatre and Cinema Culture Through the Lens of Language Use and Socio-Economic Status

In this paper, we present our work on semantic deep mapping at scale by combining information from various sources and disciplines to study historical Amsterdam. We model our data according to semantic web standards and ground them in space and time such that we can investigate what happened at a particular time and place from a linguistics, socio-economic and urban historical perspective. In a small use case we test the spatio-temporal infrastructure for research on entertainment culture in Amsterdam around the turn of the 20th century. We explain the bottlenecks we encountered for integrating information from different disciplines and sources and how we resolved or worked around them. Finally, we present a set of recommendations and best practices for adapting semantic deep mapping to other settings