In terms of across-time change in tightness-looseness, the results of the current study show that culturaltightness in Estonia rose significantly from 2002 to 2012, although the effect size of the change was small. It is argued by several theorists as well as demonstrated by empirical research (e.g., [22, 25, 56]) that socioeconomic development in a society brings systematic and pervasive cultural changes, including changes in people ’ s basic value orientations. In 2002, when our first data were collected, Estonia was still a transitional country, on a path of re-inte- gration with Europe, with joining the EU and NATO in 2004 marking the end of the transition period . The economy grew rapidly until 2007, then declined considerably during the financial crisis of 2008 – 2009, and began to slowly rise again during 2010 – 2012. We cannot say with any certainty whether, or to what extent, the slight rise in tightness that we saw in 2012, as compared to 2002, is due to changes in social and/or economic development in Estonian soci- ety during this period. It is, however, highly likely that tightness scores rose from 2002 to 2012 because new rules and norms for behavior (the previous ones were turned upside-down when Estonia regained its independence in 1991) had finally become established. This interpretation is further supported by the item-level analysis of the TLS, which revealed that, whereas the per- ceived number of social norms had not changed from 2002 to 2012, people felt that the norms were clearer and there was more general agreement about appropriate vs. inappropriate behav- ior in 2012 than in 2002. The expected compliance with social norms had also risen signifi- cantly. Thus, this perceived clarification of social norms can, on the one hand, be viewed as an indication of cultural stabilization (e.g., related to the end of the transition phase in Estonia), but, on the other hand, can explain the rise in culturaltightness – there is now more agreement about what appropriate behaviors in this culture are.
incomplete. Of the three heterogeneity measures, only RF (β = .55) was a strong predictor of PCT. RF contributed significant explanatory power to the model (F (3, 37) = 7.25, p<.01), leading to a change in R 2 of .30. Interestingly, although ELF was not a significant predictor upon its initial entry (Step 2), it became so with the inclusion of RF (and owing to a decline in the standard error of its value from .34 to .29). The restricted statistical power of these analyses should be noted; beta weights were of moderate level for each parameter (Table 23). However, none of the three measures significantly predicted societal culture strength, as measured by either practices or values (Table 24 and Table 25). Thus, it was not necessary to test for the mediation hypothesis. Furthermore, the positive relationship between RF and PCT indicates that a high degree of religious heterogeneity is predictive of culturaltightness, a somewhat surprising result. This result provides further evidence that the culturaltightness construct needs further conceptual development. Likewise, heterogeneity does not appear to be the complicating factor it was hypothesized to be. Clearly, ethnic and religious pluralism are not equivalent (see Table 22). Disregarding the problems with culturaltightness
Exploratory factor analysis is conducted due to the call of Alsos et al. (2014) for further development of the measurement scale that measures each of the total of 10 principles that effectuation and causation entail. On top of that, we used the exploratory factor analysis because scholars have suggested that it is interesting to examine how these concepts are related to other antecedent variables (e.g., Perry et al., 2012). This analysis helped to identify if the underlying constructs of culturaltightness, innovativeness and effectuation/causation align with the prescribed theories. If this is the case, hypothesis testing can be done. Before the factor analysis has been done, the author determined whether the data satisfies the necessary requirements for factor analysis. Following this, the negatively worded questions are considered and recoded. Also, based on a sample size of 109 respondents a factor analysis is appropriate (Hair, Anderson, Tatham, & Black, 1995). On top of that, the correlations (i.e., R) between the items should be at least .30 (Tabachnick & Fidell, 2007). In this study, we followed the categorization of Hair et al. (1995) whereby a factor loading of .30 is minimal, .40 important and .50 practically significant. Appendix 5 shows that only part of the items correlates enough within the given scale. However, the determinant in the correlation matrix shows that, with a score of .001, the items are still appropriate to use in a factor analysis. There is also no multicollinearity since there are no variables that have a variance inflation factor (VIF) that exceeds the threshold of five (Hair, Ringle, & Sarstedt, 2011). Using the KMO and Bartlett’s test of sphericity determined that factor analysis is appropriate since it exceeds the threshold of .50 and a significant test of sphericity (P < .05) (Hair et al., 1995; Tabachnick & Fidell, 2007). Given that all the conditions are met, an exploratory factor analysis seems appropriate.
This study found a relationship between perceived culturaltightness and the use of causation. Which corresponds to the theory of Sarasvathy (2001) where in uncertain environments an effectuation approach is more common. Since the South African national culture can be described as rather tight than loose, the environment can be seen as more certain. Which fits well in the causation approach. Gelfand et al. (2011) claim that in tight perceived cultures individuals may feel more behavioural limits which increase their uncertainty. This study found empirical proof for this statement. There is a relationship between the perceived tightness and the level of personal uncertainty of an entrepreneur. In tight perceived national cultures, entrepreneurs will experience a higher level of personal uncertainty than countries who are perceived as loose. The amount of personal uncertainty also influenced the use of causation. A person who is experiencing a high level of uncertainty prefer the usage of causation. This corresponds with a study of McGregor et al. (2001) which showed that uncertain people prefer to set specific goals and are more planned. The level of uncertainty may also be influenced by both the political and economic instability with which the country is still struggling.
Within today’s uncertain business environment an entrepreneur brings innovation, creativity and economic coordination to the economy. How do entrepreneurs set up their business? How do they make decisions? The literature proposes two approaches of entrepreneurial decision-making: causation and effectuation. An individual following the causation process is goal-oriented, focuses on expected returns, emphasizes competitive analysis, exploits pre-existing knowledge and attempts to predict the uncertain future. In contrast, an individual following the effectuation strategy is means-oriented, focuses on affordable loss, emphasizes strategic alliances, exploits contingencies and attempts to control the unpredictable future. Nevertheless, these two approaches are not substitutional, but rather complementary. Either one approach might fit to different situations and different contexts. In addition, the term ‘effectuation’ arose out of a study focusing on expert entrepreneurs. So, how do novice entrepreneurs make their decisions? Which factors do influence their decision-making process? One aspect, which might influence this process, is the national culture of the individual. As norms and values of a society shape its members’ behavior, this study focused on the cultural looseness-tightness and relates it to the decision- making processes of causation and effectuation. A tight culture can be explained as a culture with many norms and values, and with a low tolerance of deviant behavior. Whereas a loose culture is the opposite: less norms and values and high tolerance towards deviant behavior.
During the industrial age the developed world was characterized by big businesses and mass production. Nowadays, with the world being increasingly globally connected it has given way to a so called entrepreneurial economy, which focuses especially on the service sector, technological advances, demographic shifts and the availability of capital. This has encouraged start-up ventures to challenge conventional wisdom and experiment with new approaches to the market. Consequently, entrepreneurship as a subject has caused an increasing amount of interest and attention among researchers in the last decade. A new concept, namely effectuation was introduced to entrepreneurial literature. When using an effectual approach, the entrepreneurs look for opportunities to employ their actual and limited resources in an uncertain environment and in situation which future is unpredictable. The second angle which has been distinguished with regards to decision-making processes in new ventures, is causation. Causation takes an effect as given and focuses on selecting between means to create that effect. The concepts of effectuation and causation are integral parts of human reasoning that can occur simultaneously, overlapping and intertwining. Entrepreneurs use both models. The purpose of this research is to first investigate the perceived culturaltightness-looseness of the nations and following to measure the influence it has on the use of causal or effectual reasoning. Cultures that are tight, have many strong norms and a low tolerance of deviant behavior versus loose cultures, that have weak social norms and a high tolerance of deviant behavior. This paper aims at contributing to the existing literature and at expanding previous work by gathering and analyzing data from an additional country. Mexico as a Latin American and as a developing country can significantly contribute to further understand how decision making is made by analyzing the perceived influence of a tight or loose cultural perception on entrepreneurial decision making.
The degree of culturaltightness, being the strength of norms and degree of tolerance for deviance, is determined by several distal ecological and human made threats. In response to those threats, social institutions and practices are developed which together shape the tightness of a culture (Gelfand et al., 2011; Uz, 2015). A high occurrence of threats, be it natural or man-made, would increase the need for strong social norms and punishment of deviant behavior. This allows better social coordination, and a higher chance of survival. It is important, Gelfand et al. (2011) state, to refrain from value judgements on the desirability of a tight or loose culture from one’s own vantagepoint. This because the these cultural aspects are in part functional in their own ecological and historical context. Figure (2.5) shows how a combination of institutional, ecological and historical factors coupled with more mundane situational aspects affect the development of societal norms and tolerance of
This paper addresses this question and aims to provide a theoretical explanation for the frequent tightness of LP relaxations in the context of structured prediction. In particular, we show that an approximate training objective, although designed to produce accurate predictors, also induces tightness of the LP relaxation as a byproduct. Interestingly, our analysis also suggests that exact training may have the opposite effect. To explain tightness on future (i.e., test) instances, we prove several generalization bounds relating average tightness on the training data to expected tightness with respect to the generating distribution. Our bounds imply that if many predictions on training instances are tight, then predictions on test instances are also likely to be tight. Moreover, if predictions on training instances are close to integral solutions (in terms of L1 distance), then predictions on test instances will likely be similarly close to integral solutions. Our results help to understand previous empirical findings, and to our knowledge provide the first theoretical justification for the widespread success of LP relaxations for structured prediction in settings where the training data is not (algorithmically) separable.
We propose a novel approach to segmentation for Chinese IR which is based on the tight- ness measure. Our segmenter revises the out- put of a general segmenter according to the tight- ness of units. The intuition behind our method is that segmentation based on tightness of units will lead to better IR performance. For exam- ple, keeping “d d d d” (Pinatubo) as a unit should lead to better results than segmenting it into “d(skin) |d(include) |d(picture) |d(large)”. On the other hand, segmenting the compositional phrase “dddd” (Kuwait country) into “dd d(Kuwait) |d(country)” can improve recall. We revise an initial segmentation in two steps: first, we combine components that should not have been separated, such as “dddd” (Pinatubo); second, we split units which are compositional, such as “dddd” (Kuwait country).
There are over a quarter of a million houses in Ireland built before 1944 (approximately 16% of building stock – ) that can largely be considered to be of traditional construction. These buildings are typically of architectural and historic interest and it is essential that any thermal upgrading does not undermine their special character. The Irish building regulations relating to conservation of fuel and energy for buildings (Technical Guidance Document - Part L 2011) acknowledge that minimising energy requirements for the operation of existing dwellings can affect the character of buildings of architectural or historic interest and that proposed works should be carefully assessed . In relation to air tightness, the standard identifies a performance level of 7 m3/hm2 as an upper limit for air permeability for new builds although there is currently no minimum airtightness standard when upgrading existing dwellings.
Probabilistic context-free grammars have the unusual property of not always defining tight distributions (i.e., the sum of the “probabili- ties” of the trees the grammar generates can be less than one). This paper reviews how this non-tightness can arise and discusses its im- pact on Bayesian estimation of PCFGs. We begin by presenting the notion of “almost ev- erywhere tight grammars” and show that lin- ear CFGs follow it. We then propose three dif- ferent ways of reinterpreting non-tight PCFGs to make them tight, show that the Bayesian es- timators in Johnson et al. (2007) are correct under one of them, and provide MCMC sam- plers for the other two. We conclude with a discussion of the impact of tightness empiri- cally.
The remainder of this paper is organized as fol- lows. Section 2 summarizes related work. Section 3 describes four datasets of short texts. In Sec- tion 4, we describe the similarity metrics and clus- tering methods used in our experiments, as well as the evaluation measures. Section 5 shows that semantics-based similarity metrics have some ad- vantage when clustering short texts from the most creative dataset, but ultimately do not perform the best when graph-based clustering is an option. In Section 6, we demonstrate the powerful effect that tightness of clusters has on the best combination of similarity metric and clustering method for a given dataset. Finally, Section 7 draws conclusions. 2 Related Work
Air leakage (air infiltration/exfiltration commonly known as draughts) are air currents caused by air movement into and out of a building. In temperate climates, a common problem is the displacement of warm internal air by cool external air lowering room temperatures. This air leakage is caused by air pressure differences between the interior and exterior of the building. Air tightness has a large effect on heat energy consumption [4 referring to 5] and air movement can also reduce occupant’s thermal comfort [4 referring to 5, 6 and 7]. Furthermore, air leakage can cause moisture transfer and accumulation within
Contact tightness is significant in today’s diverse blend of modern society. We can see it on almost every corner of modern society, from developing and carrying out large external activities of the country’s foreign policy, to small affairs like business marketing and interpersonal communica- tion. This paper aims to study the China’s economic ties with the world’s major political and eco- nomic tightness, which can help us make a comprehensive measure of China’s international influ- ence. In addition, China’s strengths and weaknesses can be measured more accurately and ex- haustively.
Posterior shoulder tightness has been proposed to contribute to or cause a myriad of shoulder conditions including subacromial pain syndrome (SAPS), glenohumeral internal rotation deficit (GIRD), rotator cuff tears, and labral lesions [1-8]. More specifically, tightness of the posteroinferior capsule and the teres minor and infraspinatus muscles of the posterior cuff has been hypothesized to cause osteokinematic [e.g., limited glenohumeral (GH) internal rotation] and arthrokinematic dysfunctions (e.g., decentralization of the humeral head) . Tightness or shortening of these posteroinferior (PI) structures have been suggested to cause the PI capsuloligamentous complex to lose its hammock-like effect resulting in more significant pressure on the PI aspect of the head of the humerus thus causing it to migrate in an anterosuperior direction towards the coracoid process, the coracoacromial ligament and the 2 o’clock position of the glenoid labrum during arm elevation [8-11]. This anterosuperior translation has been proposed to result in a reduction in the acromiohumeral distance (AHD), potentially causing compression and impingement of the supraspinatus and subscapular muscles as well as the long head of biceps tendon and subacromial bursa [12- 13]. In addition to limited GH internal rotation (IR), the anterosuperior humeral head migration may limit shoulder flexion and horizontal adduction .
Notice that the tightness of the inequality proven in Lemma 1 does not automatically imply that the inequalities in The- orems 1–3 are tight, because Eqs. (B7)–(B9) are stronger constraints than Eq. (B1) with the relevant value of ˜ p. Since Eqs. (B7) and (B8) reflect an infinite number of operational equivalences (one for each value of x), for Theorems 1 and 2 this issue cannot be straightforwardly settled using the tech- niques from  alone because those only apply to finite sets of equivalences. It may be possible to gain some confidence by using a series of increasingly fine-grained but nevertheless finite operational equivalences. Theorem 3 is a somewhat easier case: since it is intended to apply to a finite number of outcomes, for each number of outcomes there will in fact be a finite set of equivalences for which the relevant polytope could be calculated. In this work we leave the tightness of the inequalities in Theorems 1–3 as open problems, but we find the tightness of the inequality in Lemma 1 quite suggestive. APPENDIX C: PROOF OF THEOREM 4 (PLUS EXTENSION
On this tightness continuum, at one extreme are non-compositional semantic units, such as idioms, non-compositional compounds, and transliterated names; at the other end are purely con- secutive words which means there is no dependency relation between those words, with composi- tional compounds and phrases in between. Figure 1 shows some examples of English and Chinese multi-gram semantic units along this tightness continuum, where the left end is tightest and the right end is loosest. For English, “going Dutch” is a non-compositional idiomatic expression as its meaning has nothing to do with combination of the literal meanings of “going” and “Dutch”; the same holds for “milky way,” a non-compositional compound; “machine learning” is a compo- sitional compound but a tight one as compared to “plum pie” which is significantly looser; “last year” is a common sense phrase with “last” as a modifier of “year”; “CPU can’t” is a phrase in a text with an arbitrary nominal CPU preceding the very general modal “can.” For Chinese, “ ddd d ” (match maker) is a non-compositional idiomatic expression since its meaning has nothing to do with combination of the literal meaning of “ dd ” (under the moon) and “ dd ” (old people); “ dddd ” (Urumchi) is a non-compositional transliterated proper noun; “ dddd ” (machine learning) is a compositional compound; “ d ddd ” (legitimate income) is a phrase; and “ dd dd ” (Shanghai where) are two consecutive words.