Understanding Responses to an Organizational Takeover: Introducing The Social Identity Model of Organizational Change

This paper presents a Social Identity Model of Organizational Change (SIMOC) and tests this in the context of employees’ responses to a corporate takeover. This model suggests that employees will identify with the newly emerging organization and adjust to organizational change more successfully the more they are able to maintain their pre-existing social identity (an identity maintenance pathway) or to change understanding of their social identity in ways that are perceived as constituting identity gain (an identity gain pathway). We examine this model in the context of an acquisition in the pharmaceutical industry where 225 employees were surveyed before the implementation of the organizational change and then again 18 months later. In line with SIMOC, pre-change identification predicted post-change identification and a variety of beneficial adjustment outcomes for employees (including job satisfaction, organizational citizenship behavior, lower depression, satisfaction with life, and post-traumatic growth) to the extent that either (a) they experienced a sense of identity continuity or (b) their supervisors engaged in identity leadership that helped to build a sense that they were gaining a new positive identity. Results showed a negative impact of pre-change organizational identification on post-change identification and various adjustment outcomes if both pathways were inaccessible, thereby contributing to employees’ experience of social identity loss. Discussion focuses on the ways in which organizations and their leaders can better manage organizational change and associated identity transition.

Spain’s LGBT Movement

The LGBT movement has been successful in improving the legal and social standing of sexual minorities in Spain; this includes the recognition of same-sex marriages, joint adoption, and the right to change identification in public registers. The movement has also contributed to a wider acceptance of LGBT diversity at the societal level. LGBT mobilizations in Spain started in the 1970s, with the transition toward democracy. The first political generation of activists believed in gay liberation, supported revolutionary ideas, and defended street protesting. This did not prevent activists from seeking collaboration with the state, as urgent legal action was required to end the criminalization of homosexual relations. After a decade of demobilization, a new generation of activists revamped LGBT activism in Spain during the 1990s, again with a well-defined political agenda: reacting to the devastation caused by AIDS, and also to the changes taking place in the international stage, the new “proud” generation demanded not only individual rights, but also family rights. The legalization of same-sex marriage (and joint adoption) in 2005 was the outcome of a vibrant cycle of mobilization. Contrary to some expectations, the Spanish LGBT movement has not become the victim of its own success. By shifting its attention toward the goal of substantive equality and by reaching out to new communities, the movement remains influential and vigilant against threats posed by the consolidation of new forms of conservative countermobilization.

Updating the dictionary: Semantic change identification based on change in bigrams over time

We investigate a method of updating a Danish monolingual dictionary with new semantic information on already included lemmas in a systematic way, based on the hypothesis that the variation in bigrams over time in a corpus might indicate changes in the meaning of one of the words. The method combines corpus statistics with manual annotations. The first step consists in measuring the collocational change in a homogeneous newswire corpus with texts from a 14 year time span, 2005 through 2018, by calculating all the statistically significant bigrams. These are then applied to a new version of the corpus that is split into one sub-corpus per year. We then collect all the bigrams that do not appear at all in the first three years, but appear at least 20 times in the following 11 years. The output, a dataset of 745 bigrams considered to be potentially new in Danish, are double annotated, and depending on the annotations and the inter-annotator agreement, either discarded or divided into groups of relevant data for further investigation. We then carry out a more thorough lexicographical study of the bigrams in order to determine the degree to which they support the identification of new senses and lead to revised sense inventories for at least one of the words Furthermore we study the relation between the revisions carried out, the annotation values and the degree of inter-annotator agreement. Finally, we compare the resulting updates of the dictionary with Cook et al. (2013), and discuss whether the method might lead to a more consistent way of revising and updating the dictionary in the future.

Identification of Co-Changed Classes in Software Applications Using Software Quality Attributes

When changes are made to software applications often, defects can occur in software applications, and eventually leads to expensive operational faults. Comprehensive testing is not feasible with the limited time and resources available. There is a need for test case selection and prioritization so that testing can be completed with maximum confidence in a minimum time. Advance knowledge of co-changed classes in software applications can be very useful during the software maintenance phase. In this article, the authors have proposed a co-change prediction model based upon the combination of structural code measures and dynamic revision history from change repository. Univariate analysis is applied to identify the useful measures in co-change identification. The proposed model is validated using eight open source software applications. The results are promising and indicate that they can be very beneficial in maintenance of software applications.

A method of assessing rework for implementing software requirements changes

Software development is often affected by user/system requirements changes. To implement requirements changes, a system which is being developed needs to be reworked. However, the term ?Rework? has not been clearly defined in the literature. Depending on the complexity of the changes, the amount of rework required varies from some software module modifications to a non-trivial alteration to the software design of a system. The effort associated with such a rework obviously will vary too. To date, there has been scant research on rework assessment, and the relationship between it and change effort estimation is hardly understood. In this paper, we present a definition for rework, and describe a method of assessing rework for implementing software requirements changes. Our method consists of three stages: namely (i) change identification; (ii) change analysis; and (iii) rework assessment. To demonstrate the practicality that it enables developers to compare the rework between the different options available for implementing a requirements change and to identify the one which is less invasive and requires lesser amount of modifications to the software system design, we explain our concept with the use of a running example.

How different are different diff algorithms in Git?

Automatic identification of the differences between two versions of a file is a common and basic task in several applications of mining code repositories. Git, a version control system, has a diff utility and users can select algorithms of diff from the default algorithm Myers to the advanced Histogram algorithm. From our systematic mapping, we identified three popular applications of diff in recent studies. On the impact on code churn metrics in 14 Java projects, we obtained different values in 1.7% to 8.2% commits based on the different diff algorithms. Regarding bug-introducing change identification, we found 6.0% and 13.3% in the identified bug-fix commits had different results of bug-introducing changes from 10 Java projects. For patch application, we found that the Histogram is more suitable than Myers for providing the changes of code, from our manual analysis. Thus, we strongly recommend using the Histogram algorithm when mining Git repositories to consider differences in source code.