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The preponderance of studies that rely on self-report for both independent (e.g. stressors) and dependent (e.g. well-being) variables is often deplored, as it creates problems of common method variance, which may lead to inflated, or even spurious, correlations and predictions. It is sometimes suggested that alternative measures should yield more “objective” information on the phenomena under investigation. We discuss this issue with regard to: (a) observational measures of working conditions; (b) physiological measures of strain; and (c) event-based “self-observation” on a micro-level. We argue that these methods are not necessarily “objective.” Like self-report, they are influenced by a plethora of factors; and measurement artifacts can easily be produced. All this can make their interpretation quite difficult, and the conclusion that lack of convergence with self-report automatically invalidates self-report is not necessarily warranted. Especially with regard to physiological measures, one has to keep in mind that they refer to a different response level that follows its own laws and is only loosely coupled with psychological responses. Therefore, replacement is not a promising way to get more reliable estimates of stressor-strain relationships. We argue instead that each method contains both substantive and error variance, and that a combination of various methods seems more auspicious. After discussing advantages and pitfalls of observational, physiological, and self-observational measures, respectively, we report empirical examples from our own research on each of these methods, which are meant to illustrate both the advantages and the problems associated with them. They strengthen the overall conclusion that there is no “substitute” for self-report (which often is necessary to be able to interpret data from other methods, most notably physiological ones). They also illustrate that collecting such data is quite cumbersome, and that a number of conditions have to be carefully considered before using them, and we report some problems we encountered in this research. Altogether, we conclude that self-report measures, if carefully constructed, are better than their reputation, but that the optimal way is to complement them with other measures.

In this paper, I show how a consideration of legitimacy processes is of theoretical use in addressing two current issues in status research. First, I investigate under what conditions the contrast between the sex composition of a work group and the sex composition of an organization’s authority structure may trigger the salience of gender status in task groups. I argue that this contrast will make gender status salient when an evaluation from an authority figure outside the group creates inconsistency and uncertainty in the current status structure within the group. Delegitimation of a superior is one such process that produces this inconsistency and uncertainty. Second, I examine under what conditions status position compared to identity will more likely stimulate behavior among work group members. I argue that the legitimation of the superior and the group’s status order reduce the likelihood that group members will pursue status inconsistent, identity behaviors. Delegitimation, however, increases opportunities for acting in identity consistent ways and reduces the costs for doing so, thus enhancing the likelihood of identity-based behaviors.

In this chapter, I discuss intersectionality in relation to complexity theory as an approach to social systems, inspired by Sylvia Walby's ground-breaking work where she brings together these two theoretical perspectives (2007, 2009, p. 250). In order to apply her synthesis to my study of migrant nurses to Norway, I examine the methodological potential of Bourdieu-inspired feminist concept of capitals (Adkins & Skeggs, 2004) in grasping the connections between individual agency and intersecting systems of inequality.

With the advance of the Silk Road Initiative proposed by China, it has been a focus of China government to develop strategic emerging industries. The development of strategic emerging industries needs the support of competitive intelligence on many aspects such as strategical planning, policy making, industrial structure adjustment, and technology innovation. However, so far there are few studies toward the competitive intelligence systems for strategic emerging industries. In this article, we focus on a number of issues related to the competitive intelligence for strategic emerging industries in China. First, we conduct a strengths, weaknesses, opportunities, and threats analysis on the situations of strategic emerging industries in China, based on which the necessity of building a competitive intelligence (CI) service system for strategic emerging industries is discussed. Next, the authors present a framework of a CI service system for strategic emerging industries in China. The principles, components, working process, and product forms are deeply described. The CI service system proposed in this article consists of a cooperation network platform, three layered organizations, and three systems, which integrates organizations, information, people, network, and service platforms into an ecosystem to offer competitive intelligence supports for government, industry, and enterprises. Finally, the authors discuss a case study of the proposed CI service system for the new energy automobile industry.