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Departing from previous research which shows that abusive supervision, as a salient job demand, induces detrimental employee outcomes, this study examines how to create constructive consequences of abusive supervision. To do so, the authors identify the boundary conditions to change the negative effect of supervisory abuse on employees’ work engagement in a positive direction. The authors examine the interactive moderating effect of a personal resource (i.e. positive causal attribution of abusive supervision) and a job resource (i.e. workplace friendship) on the relationship between abusive supervision and work engagement.

The authors used data from a two-wave survey of 697 full-time workers with a time interval of one month and conducted three-way interaction analyses to test their hypothesized model.

Abusive supervision increases employees’ work engagement when they make a positive causal attribution of abusive supervision (i.e. interpreting their abusive supervisor’s motives as promoting their job performance, rather than as intentionally harming them) and have favorable workplace friends.

The authors study offers a novel picture of abusive supervision by revealing that supervisory abuse can enhance employees’ work engagement when it is coupled with proper personal and job resources. In addition, this study highlights that in order to identify constructive effects of abusive supervision, it is critical to delve into the interaction between resources from these two domains to deal with abusive supervision.

The purpose of this paper is to delve into perceived underqualification, which refers to employees’ awareness that they have deficient abilities relative to their job demands (abilities<demands). In examining person–job (P–J) misfit, previous research has primarily focused on one type of misfit, overqualification (abilities>demands), leaving the other type, underqualification, unexplored. To address the neglect, this study investigates how perceived underqualification relates to job attitudes (job satisfaction, organizational commitment, and turnover intention) and how transformational leadership moderates the relationships.

Survey data were collected from employees working at diverse organizations in South Korea over two waves; at Time 1, perceived underqualification and transformational leadership were measured and at Time 2, job attitude variables were measured. Responses from 188 employees were used for hypothesis testing.

Perceived underqualification is negatively related to job satisfaction and organizational commitment and positively related to turnover intention when transformational leadership is low. However, under high transformational leadership, such negative attitudinal implications of perceived underqualification are weakened.

By examining underqualification for the first time, this study corrects the current incomplete and biased understanding of P–J misfit, which is exclusively overqualification-focused. In addition, this study provides new insight into individual responses to P–J misfit by revealing that the responses are not always negative. This study specifies transformational leadership as the contingency factor that enables such responses, thus further advancing the P–J misfit literature that has hardly examined the leadership effect.

While conceived to examine key factors affecting post-retirement career advancement of retired elite athletes in South Korea, the purpose of this paper is to report how English, as a de facto global lingua franca, functions as a powerful gatekeeper in the sports administration field.

Interpreted through the lens of Bourdieu’s linguistic capital and Gramsci’s hegemony of language, the present study draws on content analysis of semi-structured individual interviews, as well as focus group interviews, conducted with thirty former South Korean elite athletes.

Based on the data analysis, systematic bias toward athletes was uncovered, privileging English as the single determining factor for employment. Furthermore, the educational implications for adult learners of English as a Foreign or English an Additional Language reveal unrealistic expectations of top–down language policies.

Perspectives of athlete participants, an underrepresented group in educational research, within the South Korean globalization context shed critical light on the pervasive aspects of English hegemony and its unexamined dimensions.

Social media networks like Twitter, Facebook, WhatsApp etc. are most commonly used medium for sharing news, opinions and to stay in touch with peers. Messages on twitter are limited to 140 characters. This led users to create their own novel syntax in tweets to express more in lesser words. Free writing style, use of URLs, markup syntax, inappropriate punctuations, ungrammatical structures, abbreviations etc. makes it harder to mine useful information from them. For each tweet, we can get an explicit time stamp, the name of the user, the social network the user belongs to, or even the GPS coordinates if the tweet is created with a GPS-enabled mobile device. With these features, Twitter is, in nature, a good resource for detecting and analyzing the real time events happening around the world. By using the speed and coverage of Twitter, we can detect events, a sequence of important keywords being talked, in a timely manner which can be used in different applications like natural calamity relief support, earthquake relief support, product launches, suspicious activity detection etc. The keyword detection process from Twitter can be seen as a two step process: detection of keyword in the raw text form (words as posted by the users) and keyword normalization process (reforming the users’ unstructured words in the complete meaningful English language words). In this paper a keyword detection technique based upon the graph, spanning tree and Page Rank algorithm is proposed. A text normalization technique based upon hybrid approach using Levenshtein distance, demetaphone algorithm and dictionary mapping is proposed to work upon the unstructured keywords as produced by the proposed keyword detector. The proposed normalization technique is validated using the standard lexnorm 1.2 dataset. The proposed system is used to detect the keywords from Twiter text being posted at real time. The detected and normalized keywords are further validated from the search engine results at later time for detection of events.