Understanding supply chain disruption risk with the aid of social networks and information flows analysis

Supply Chain Risk Management (SCRM) has become a popular area of research and study in recent years. This can be highlighted by the number of peer reviewed articles that have appeared in academic literature. This coupled with the realisation by companies that SCRM strategies are required to mitigate the risks that they face, makes for challenging research questions in the field of risk management. The challenge that companies face today is not only to identify the types of risks that they face, but also to assess the indicators of risk that face them. This will allow them to mitigate that risk before any disruption to the supply chain occurs. The use of social network theory can aid in the identification of disruption risk. This thesis proposes the combination of social networks, behavioural risk indicators and information management, to uniquely identify disruption risk. The propositions that were developed from the literature review and exploratory case study in the aerospace OEM, in this thesis are:- By improving information flows, through the use of social networks, we can identify supply chain disruption risk. - The management of information to identify supply chain disruption risk can be explored using push and pull concepts. The propositions were further explored through four focus group sessions, two within the OEM and two within an academic setting. The literature review conducted by the researcher did not find any studies that have evaluated supply chain disruption risk management in terms of social network analysis or information management studies. The evaluation of SCRM using these methods is thought to be a unique way of understanding the issues in SCRM that practitioners face today in the aerospace industry.

Entrepreneurial intention and entrepreneurial behaviour:a social psychological perspective

Increasing the supply of entrepreneurs reduces unemployment and accelerates economic growth (Acs, 2006; Audretsch, 2007; Santarelli et el. 2009; Campbell, 1996; Carree & Thurik, 1996). The supply of entrepreneurs depends on the entrepreneurial intention and activity of the people (Kruger & Brazeal, 1994). Existing behavioural theories explain that entrepreneurial activity is an attitude driven process which is mediated by intention and regulated by behavioural control. These theories are: Theory of Planned Behaviour (Ajzen, 1991; 2002, 2012); Entrepreneurial Event Model (Shapiro & Shokol, 1982), and Social Cognitive Theory (Bandura, 1977; 1986; 2012). Meta-analysis of existing behavioural theories in different fields found that the theories are more effective to analyse behavioural intention and habitual behaviour, but less effective to analyse long-term and risky behaviour (McEachan et al., 2011). The objective of this dissertation is to improve entrepreneurship behaviour theory to advance our understanding of the determinants of the entrepreneurial intention and activity. To achieve this objective we asked three compelling questions in our research. These are: Firstly, why do differences exist in entrepreneurship among age groups. Secondly, how can we improve the theory to analyse entrepreneurial intention and behaviour? And, thirdly, is there any relationship between counterfactual or regretful thinking and entrepreneurial intention? We address these three questions in Chapters 2, 3 and 4 of the dissertation. Earlier studies have identified that there is an inverse U shaped relationship between age and entrepreneurship (Parker, 2004; Hart et al., 2004). In our study, we explain the reasons for this inverse U shape (Chapter 2). To analyse the reasons we use Cognitive Life Cycle theory and Disuse theory. We assume that the stage in the life cycle of an individual moderates the influence of opportunity identification and skill to start a business. In our study, we analyse the moderation effect in early stage entrepreneurship and in serial entrepreneurship. In Chapter 3, the limitations of existing psychological theories are discussed, and a competency value theory of entrepreneurship (CVTE) is proposed to overcome the limitations and extend existing theories. We use a ‘weighted competency’ variable instead of a ‘perceived behavioural control’ variable for the theory of planned behaviour (TPB) and self-efficacy variable for social cognitive theory. Weighted competency is the perceived competency ranking assigned by an individual for his total competencies to be an entrepreneur. The proposed theory was tested in a pilot survey in the UK and in a national adult population survey in a South Asian Country. The results show a significant relationship between competencies and entrepreneurial intention, and weighted competencies and entrepreneurial behaviour as per CVTE. To improve the theory further, in Chapter 4, we test the relationship between counterfactual thinking and entrepreneurial intention. Studies in cognitive psychology identify that ‘upward counterfactual thinking’ influences intention and behaviour (Epstude & Rose, 2008; Smallman & Roese, 2009). Upward counterfactual thinking is regretful thinking for missed opportunities of a problem. This study addresses the question of how an individual’s regretful thinking affects his or her future entrepreneurial career intention. To do so, we conducted a study among students in a business school in the UK, and we found that counterfactual thinking modifies the influence of attitude and opportunity identification in entrepreneurial career intention.

The antecedents and consequences of anticipatory identification

The aim of this thesis was to investigate anticipatory identification: newcomers’ identification with an organisation prior to entry; in particular by exploring the antecedents and consequences of the construct. Although organisational identification has been frequently investigated over the past 25 years, surprisingly little is known about what causes an individual to identify with a new organisation before entry and whether this has an impact on their relationship with the organisation after formally taking up membership. Drawing on a Social Identity approach to organisational identification, it was hypothesised that newcomers would more closely identify with an organisation prior to entry when the organisation was seen as a source of positive social identity and was situationally relevant and meaningful to the newcomer, i.e. salient, during the pre-entry period. It was also hypothesised that anticipatory identification would have post-entry consequences and would predict newcomers’ post-entry identification, turnover intentions and job satisfaction. An indirect relationship between anticipatory identification and post-entry identification through post-entry social identity judgements (termed a “feedback loop” mechanism) was additionally proposed. Finally anticipatory identification was also predicted to moderate the relationship between post-entry social identity judgements and post-entry identification (termed a “buffering” mechanism). Four studies were conducted to test these hypotheses. Study One served as a pilot study, using a retrospective self-report design with s sample of 124 university students to initially test the proposed conceptual model. Studies Two and Three adopted experimental designs. Each used a unique sample of 72 staff and students from Aston University to respectively test the hypothesised positive social identity motive and salience antecedents of anticipatory identification. Study Four explored the relationship between anticipatory identification, its antecedents and consequences longitudinally, using an organisational sample of 45 employees. Overall, these studies found support for a social identity motive antecedent of anticipatory identification, as well as more limited evidence that anticipatory identification was associated with the salience of an organisation prior to entry. Support was inconsistent for a direct relationship between anticipatory identification and post-entry identification and there was no evidence that anticipatory identification was a significant direct predictor of turnover intention and job satisfaction. Anticipatory identification was however found to act as a buffer in the relationship between post-entry social identity judgements and post-entry identification in all but one of the four samples measured. A feedback loop mechanism was observed within the experimental designs of Studies Two and Three, but not within the organisational samples of Studies One and Four. Overall the findings of these four studies highlight key ways through which anticipatory identification can develop prior to entry into an organisation. Moreover, the research observed several important post-entry consequences of anticipatory identification, indicating that an understanding of post-entry identification may be enriched by attending more closely to the extent to which newcomers identify with an organisation prior to entry.

It's the way you check-in:identifying users in location-based social networks

In recent years, the rapid spread of smartphones has led to the increasing popularity of Location-Based Social Networks (LBSNs). Although a number of research studies and articles in the press have shown the dangers of exposing personal location data, the inherent nature of LBSNs encourages users to publish information about their current location (i.e., their check-ins). The same is true for the majority of the most popular social networking websites, which offer the possibility of associating the current location of users to their posts and photos. Moreover, some LBSNs, such as Foursquare, let users tag their friends in their check-ins, thus potentially releasing location information of individuals that have no control over the published data. This raises additional privacy concerns for the management of location information in LBSNs. In this paper we propose and evaluate a series of techniques for the identification of users from their check-in data. More specifically, we first present two strategies according to which users are characterized by the spatio-temporal trajectory emerging from their check-ins over time and the frequency of visit to specific locations, respectively. In addition to these approaches, we also propose a hybrid strategy that is able to exploit both types of information. It is worth noting that these techniques can be applied to a more general class of problems where locations and social links of individuals are available in a given dataset. We evaluate our techniques by means of three real-world LBSNs datasets, demonstrating that a very limited amount of data points is sufficient to identify a user with a high degree of accuracy. For instance, we show that in some datasets we are able to classify more than 80% of the users correctly.

Analysis and identification of spamming behaviors in Sina Weibo microblog

Spamming has been a widespread problem for social networks. In recent years there is an increasing interest in the analysis of anti-spamming for microblogs, such as Twitter. In this paper we present a systematic research on the analysis of spamming in Sina Weibo platform, which is currently a dominant microblogging service provider in China. Our research objectives are to understand the specific spamming behaviors in Sina Weibo and find approaches to identify and block spammers in Sina Weibo based on spamming behavior classifiers. To start with the analysis of spamming behaviors we devise several effective methods to collect a large set of spammer samples, including uses of proactive honeypots and crawlers, keywords based searching and buying spammer samples directly from online merchants. We processed the database associated with these spammer samples and interestingly we found three representative spamming behaviors: Aggressive advertising, repeated duplicate reposting and aggressive following. We extract various features and compare the behaviors of spammers and legitimate users with regard to these features. It is found that spamming behaviors and normal behaviors have distinct characteristics. Based on these findings we design an automatic online spammer identification system. Through tests with real data it is demonstrated that the system can effectively detect the spamming behaviors and identify spammers in Sina Weibo.

Making the organization fly:organizational identification and citizenship in full-service and low-cost airlines

Recently, the service industry has seen a low-cost sector emerge alongside the traditional full-service sector. We explored whether these business models have different implications for employee cooperation, one factor that plays an important role in organizational functioning. Drawing on the social identity perspective, we argue that employees will identify less strongly with the lower-status, low-cost organizations, reducing their intrinsic motivation for such cooperation. We tested these relationships among employees in Thailand's airline industry. In line with expectations, flight attendants working for low-cost airlines (N = 77) perceived their organizations to have lower status than those working for the full-service airlines (N = 77), and this was associated with reduced organizational identification. This in turn predicted lower levels of organizational citizenship behaviour and a stronger desire for organizational exit. © 2010 Hogrefe Publishing.