The Effect of Sleep Quality and Being Physically Active on Developing Mental Toughness

Mental toughness (MT) has been increasingly associated with successful performance in several stressful and competitive environments (e.g. the military, business, academics, medicine, sports). Being physically active (PA) may compromise sleep quality (SQ). Research has reported conflicting associations regarding PA and MT. Regarding SQ and MT, a bidirectional association has been reported. However, research has not yet focused on the combined effects of PA and SQ on MT. PURPOSE: To characterize the association and the effects of PA and SQ on MT. The authors hypothesized that: (a) PA and SQ are negatively associated; (b) PA and MT are positively associated; (c) SQ and MT are negatively associated; and (d) the interaction effect of PA and SQ on MT will be buffering. METHODS: Sixty-two participants (age 25.4 6.0 SD) completed inventories related to SQ (Pittsburgh Sleep Quality Index) and MT (Mental Toughness Index). PA data were collected according to American College of Sports Medicine guidelines. Main and interaction effects of the responses were analyzed using factorial ANOVA. Significance was set at p \u3c 0.05. All analyses were performed using SPSS. RESULTS: PA was positively correlated with SQ (r = .009, p =.473) and with MT (r = .246, p = .027). SQ was negatively correlated with MT (r = -.470, p = .000). PA (F1,58 = 10.939, p = .002, η2 = .159) and QS (F1,58 = 23.051, p = .000, η2 = .284) had a main effect on MT. The interaction of PA and QS had a buffering moderating effect on MT (F1,58 = 12.394, p = .001, η2 = .176). CONCLUSION: Evidence was found for all but the first hypothesis. PA-participants tending to be mentally tougher than the non-PA ones. Poor sleepers, on average, were mentally tougher than the good sleepers. The buffering effect indicates that the non-PA individuals with poor quality of sleep are the mentally toughest ones, followed by PA individuals with poor quality of sleep. Non-PA individuals with good quality of sleep present the lowest MT levels. In regards to developing mental toughness the authors suggest that: a) PA should be prescribed to good quality of sleepers and b) in poor quality sleepers focus should be placed on sleep before PA. Such findings may be useful to exercise and health-related practitioners when prescribing PA in a wide variety of individuals that report sleep quality issues in relation to MT capacities

Family Businesses and Adaptation: A Dynamic Capabilities Approach

The main objective of this research was to propose a framework centred on the dynamic capabilities approach, and to be applied in the context of family businesses’ adaption to their changing business environment. Data were gathered through interviews with ten FBs operating in Western Australia. Based on the findings, the clusters of activities, sensing, seizing, and transforming emerged as key factors for firms’ adaptation, and were reinforced by firms’ open culture, signature processes, idiosyncratic knowledge, and valuable, rare, inimitable and non-substitutable attributes. Thus, the usefulness of the proposed framework was confirmed. Implications and future research opportunities are presented. © 2018, The Author(s)

The use of lemmas in the model elimination procedure

When the Model Elimination (ME) procedure was rst proposed, a notion of lemma was put forth as a promising augmentation to the basic complete proof procedure. Here the lemmas that are used are also discovered by the procedure in the same proof run. Several implementations of ME now exist but only a 1970's implementation explicitly examined this lemma mechanism, with indi erent results. We report on the successful use of lemmas using the METEOR implementation of ME. Not only does the lemma device permit METEOR to obtain proofs not otherwise obtainable by METEOR, or any other ME prover not using lemmas, but some well-known challenge problems are solved. We discuss several of these more di cult problems, including two challenge problems for uniform general-purpose provers, where METEOR was rst in obtaining the proof. The problems are not selected simply to show o the lemma device, but rather to understand it better. Thus, we choose problems with widely di erent characteristics, including one where very few lemmas are created automatically, the opposite of normal behavior. This selection points out the potential of, and the problems with, lemma use. The biggest problem normally is the selection of appropriate lemmas to retain from the large number generated.