본 연구에서는 일반 폴리시와 젤 폴리시의 안전성 인식 및 손톱 손상도에 대해 알아보고자 하였다. 연구방법은 본 연구에 참여를 동의한 20~50대 여성을 대상으로 사전 설문조사와 실험 연구를 진행하였다. 그 결과, 일반 폴리시와 젤 폴리시의 효과성 인식, 안전성 인식, 손톱 손상도 인식 차이에서 모두 젤 폴리시가 높게 나타났다. 실험 전·후 손톱의 수분, 유분, 경피수분손실량(TEWL)은 실험 전보다 모두 감소 하였으며, 일반 폴리시에 비해 젤 폴리시의 감소 폭이 높게 나타남을 확인하였다. 또한 손톱의 SEM 표면 관찰에서도 젤 폴리시의 손톱 표면 손상도가 매우 크게 나타남을 확인하였다. 이상과 같이 본 연구를 통해 일반 폴리시보다 젤 폴리시가 손톱의 손상도에 많은 영향을 미치는 것을 확인하였다. 따라서 소비자들의 건강하고 아름다운 손톱관리 및 유지를 위한 기초 자료로 도움이 될 수 있기를 기대한다.

Marketing literature have widely discussed the interplay between marketing capabilities (i.e. company resources and competences in marketing-mix area), market orientation, company innovativeness and company performance (e.g. Knight & Cavusgil, 2004; Luca & Atuahene-Gima, 2007; Moorman & Slotegraaf, 1999; Morgan, Vorhies, & Mason, 2009). However, these studies presented static view of marketing capabilities and resources which becomes less and less relevant in today uncertain market environments (Day, 2011). In this study we follow dynamic capability view (DCV) in management research (Eisenhardt & Martin, 2000; Teece, Pisano, & Shuen, 1997) and we distinguish dynamic marketing capabilities (DMC) from other dynamic capabilities, company resources and competencies (Barrales - Molina, Martínez - López, & Gázquez - Abad, 2014; Morgan, 2012). Specifically, we conceptualize Dynamic Marketing Reconfiguration (DMR) as a DMC grounded explicitly in dynamic capability view and we provide empirical test for a research model, where DMR is hypothesized as factor complementing Market Orientation and influencing on company product innovation success. Corresponding with contingency theory (Donaldson, 2001; Drazin & Van de Ven, 1985), in this study we hypothesize and test the idea that DMR is a company capability that is in a good “fit” with certain organizational and environmental factors. Morgan (2012) and Barrales - Molina, et al. (2014) have proposed dynamic marketing capabilities (DMC) aligned with concepts of “market knowledge” and “marketing resources” and they distinguish DMC from other company capabilities (e.g. manufacturing capabilities, SCM capabilities). Recent marketing literature illustrated empirically that there are actually various DMCs that the focal company may utilize to achieve competitive advantage. For example, Narver, Slater & MacLachlan (2004) focus on Proactive Market Orientation that enable dynamic sensing and satisfying unconscious consumer needs through new product development. Weerawardena, et al. (2015) tested the impact of global dynamic marketing capability and market focused learning on innovation performance and company early internationalization. In this study we focus on specific dynamic marketing capability that complements prior works in this area, namely Dynamic Marketing Reconfiguration (DMR). In line with DCV we conceptualize DMR as company routines oriented at transforming market knowledge into modified and new configurations of marketing resources that are better aligned with market changes. Corresponding with prior studies on static marketing resources and capabilities (Knight & Cavusgil, 2004; Moorman & Slotegraaf, 1999; Morgan, Vorhies, & Mason, 2009), we argue that DMR utilizes such static resources dynamically by systematic enriching and combining them, and,as the consequence, the company equipped with advanced DMR can improve their alignment with the market environment. We do not assume that DMR equals strategy orientating company only at new marketing resources (e.g. utilizing new market knowledge, implementing new advertising instruments, brand repositioning, entering new market segments), but rather at improved configurations of marketing resources (e.g. combing existing resources and marketing experience with new marketing tools in effective way). Our conceptualization of DMR mirrors some real marketing practices that are observable in case of companies operating successfully in regional markets and companies operating on global scale today. The spectacular illustration of DMR is Disney corporation strategy with regard to their new product “Star Wars: the Force Awakens” that was released in December 2015. Disney have implemented very dynamic, innovative and massive approach to promotion of their new product, complementing their traditional advertising tools (e.g. TV advertisement, toys) by modern instruments, such as fan events, new computer games, cartoons and PR in social media (Bart, 2015; Schwartz, 2015). The positive influence of Market Orientation (MO) on company product innovations and company performance was widely discussed in prior studies (Atuahene-Gima, 1996; Han, Kim, & Srivastava, 1998; Hurley & Hult, 1998; Kumar, Jones, Venkatesan, & Leone, 2011). Following Narver, et al. (2004), in this study we treat MO as business’s attempt to understand and to satisfy customers’ needs. Such understanding is useful at every stage of new product development. Additionally, MO does not help the company only through positive impact on product innovativeness, but also through leveraging effectiveness of all marketing resources and instruments, because they are aligned with knowledge about customer needs. Thus, we hypothesize: H1.1: The higher Market Orientation of the company, the stronger success of its product innovations. H1.2: The higher Market Orientation of the company, the better company performance. Dynamic Marketing Reconfiguration (DMR) complements company MO in attempts to improve product innovation success, because understanding and satisfying customer needs present rather static approach to marketing-market alignment. MO is not enough in today volatile markets, when company needs to constantly reconfigure marketing resources to develop and, especially commercialize, new products (Day, 2011; Barrales - Molina, et al. 2014). DMR does not only influence positively on product innovations, but it also directly influences on company performance. Systematically adjusting marketing resources to changes in market environment leverages sales of all company products, not only newly introduced ones. Therefore, we hypothesize: H2.1: The higher Dynamic Marketing Reconfiguration in the company, the stronger success of its product innovations. H2.2: The higher Dynamic Marketing Reconfiguration in the company, the better company performance. This research is focused on product (offering) innovations as the key innovation outcome of a firm’s marketing routines. Product innovation has been established in the management and strategy literature as an important driver of firm performance (e.g. Han et al., 1998). Thus, our hypothesis is:H3: The stronger Product Innovation Success, the better company performance. Companies do not operate in a vacuum, so this study follows contingency theory that postulates finding the “fit” between environmental contingencies and internal configurations within the company (Donaldson, 2001; Van de Ven & Drazin, 1984). So far, only a few studies applying dynamic capabilities perspective have discussed the role of contingencies which creates a research gap for our understanding of DCV (Barreto, 2010). Such research gap is even more visible with regard to our knowledge of dynamic marketing capabilities (DMC), because prior empirical studies in this area have largely ignored contingencies, except environmental uncertainty and firm age (Flatten, et al., 2015). Consequently, in this study we analyse the role of two contingency factors, namely: company size (internal configuration) and industry norms related to product customization (external configuration). Prior studies have presented blurred picture of the role of company size, because dynamic capabilities were also found effective in case of small enterprises (Døving & Gooderham, 2008; Salvato, 2003). Nevertheless, in this study we incorporate original DCV reasoning, because intuitively, in SMEs company routines may be replaced by other factors that are typical advantages of small scale enterprise. On the extreme point, in micro firms (e.g. below 10 employees), there is no need to standardize certain behaviours among employees at all, because all marketing actions, including planning, execution and control are performed usually by one person. Therefore, we hypothesize: H4.1: The Dynamic Marketing Reconfiguration has stronger influence on Product Innovation Success in big and medium companies in comparison to small companies. Contingency theory suggests controlling for the effects from not only organizational features, but also environmental factors (Donaldson, 2001; Van de Ven & Drazin, 1984). We follow Lampel & Mintzberg (1996) suggestion that “…some industries favour customization and some foster standardization…” (p. 21) and we assume that all industries may be distinguished into two main categories: High customization industry vs. Low customization industry. Such distinction is based on perceived dominance of customization vs. standardization practices among competing companies and it may be treated as a proxy to the popularization of relationship marketing strategy (Grönroos, 1994) and service-dominant logic (Vargo & Lusch, 2004) in a given industry. In industries, where high product customization function as a norm, the new product development works through collaborative efforts with customers and value co-creation (Hoyer, Chandy, Dorotic, Krafft, & Singh, 2010). Dynamic marketing capabilities oriented at marketing reconfiguration may be not effective in case of companies working in high customization industries, as close customer relationships popular in such industries demand customer trust and commitment rather than dynamic marketing, including utilizing newest marketing instruments (Mitrega & Katrichis, 2010; Palmatier, Dant, Grewal, & Evans, 2006). In sum, we hypothesize: H4.2: The Dynamic Marketing Reconfiguration has weaker influence on Product Innovation Success, when it is implemented in the industry that favours high product customization. We tested our hypotheses on the cross-sectional survey data based on the sample of 155 companies operating in Poland and we applied structural equation modeling(PLS-SEM) to estimate the hypothesized research model using SmartPLS 3.0 (Hair, Hult, Ringle, & Sarstedt, 2013; Ringle, Wende, & Becker, 2014). As our conceptualization of DMR is grounded in the dynamic capabilities view, we searched for such scales for this construct, which would reflect actions standardized among managers and other people involved in marketing (Eisenhardt & Martin, 2000; Teece, 2007). Thus, for DMR we have adapted selected scales proposed for dynamic capabilities by Pavlou & El Sawy (2011). The market orientation was measured in line with reactive market orientation (Narver, et al. 2004), product innovation success was measured according to Ritter and Gemünden (2003) and company performance according to Reinartz et al. (2004). We applied single item measurement for our contingency factors. Specifically, company size was measured due to number of people employed in the company and industry norms with regard to product customization were measured through question: “Please specify, if your industry demands adjusting company offering to individual requirements of a given customer (e.g. detailed negotiations, product adjustments)”. After purifying measurement model through Exploratory Factor Analysis, we retained 18 items for our 4 latent constructs. The items are available by email upon request. Our measurement model received empirical support for its validity and reliability with regard to literature suggestions, i.e. AVE > 0.5; Cronbach Alpha >0.7; Alpha and Fornell-Larcker discriminant validity criterion (Fornell & Larcker, 1981; Hair et al., 2012). We conducted PLS-SEM analysis following Hair et al. (2013) suggestions. T-statistics were computed by applying a bootstrapping procedure with 5000 bootstrap samples. The detailed results of model estimation are available upon request. Path coefficients for the research model appeared to be all significant except the link between market orientation and company performance, which supports hypotheses H1.1, H2.1; H2.2 and H3, but rejects H1.2. Additionally, contingency effects were tested using Multi-Group Analysis (MGA) implemented in SmartPLS 3.0 (Ringle, et al.2014). The differences in coefficient for path DMR -> PROD. INNOV SUCCESS were found significant in sub-samples according to company size and according to customization industry norms. Specifically, in case of medium and big companies (n = 71) the influence of DMR on product innovation success was significantly stronger (b=0.59) than in case of small companies (n=84; b = 0.28). In case of companies that did not report product customization as industry norm (n=93) the influence of DMR on product innovation success was significantly stronger (b=0.46) than in case of companies that reported industry pressure on product customization (n=62; b=0.27). Thus, all hypotheses connected with contingency effects (H4.1; H4.2) received support. Our study corresponds with recent research devoted to dynamic marketing capabilities (Flatten, et al, 2015; Weerawardena, et al. 2015) and it enriches this research by looking at DMCs from a different angle. Dynamic Marketing Reconfiguration (DMR) that we focus on embraces explicitly these company routines that transform existing marketing resources into their new combinations better aligned with market changes. Thus, DMR is different to proactive market orientation (Narver, at al. 2004) as such PMO is oriented at latent customer needs, but does not assume marketing reconfiguration, e.g. in terms of utilizing new marketing tools. In contrast to Flatten et al. (2015), DMR does not focus only on dynamic pricing capabilities, but it refers to reconfiguration of all marketing resources (i.e. pricing and other marketing-mix elements as well). Our study validates and enriches study by Weerawardena, et al.(2015), where dynamic marketing capabilities were found as the leverage for innovation performance. Similarly to this recent study, our study also confirms positive influence of dynamic marketing capability on innovation performance, but study by Weerawardena et al. (2015) was limited to early internationalizing firms in US and Australia, so we provide different empirical setting for testing this influence (i.e. companies based in Poland in various stages of their internationalization). More importantly, we qualify Weerawardena et al. (2015) by combining insights from dynamic capabilities theory (Teece et al., 1997) and contingency theory (Donaldson, 2001; Van de Ven & Drazin, 1984) and we test previously neglected contingency effects, namely: company size and industry norms with regard to product customization. Our research results suggest that dynamic marketing capabilities, namely DMR, are especially important for bigger companies and for these companies that are not under pressure for strong product customization. It may mean that DMR should be not applied in case of these companies that follow relationship marketing approach (Palmatier, Scheer, Evans, & Arnold, 2008), especially these companies that operate in B2B settings. In general, our study follows recent call for better understanding of dynamic marketing capabilities through more rigorous conceptualizations and providing tests in various empirical settings (Barrales - Molina, et al, 2014).

The article aims to quantitatively test the DART model, so far studied mostly with qualitative methods. A multiple measurement scale was employed in interviewing managers. The statistical evidence for adequacy of the model was obtained through CFA. The findings suggest that DART may not accurately reflect firms’ co-creation practices.

This study aims to investigate the learning style preferences of KFL Leaners. For this I investigate the learning style preferences of 52 Polish and 43 Chinese learners by Cohen, Oxford, and Chi's (2002) LSS (Learning Style Survey). Research questionnaires consist of 11 parts, I categorized these as Sensory Type, Personality Type, Desired Degree of Generality based on Oxford (2003). The major findings analyzed by statistical program are as follows: First, in each part, three dimensions of Concrete-Sequential vs. Random-Intuitive, Impulsive vs. Reflective, Global vs. Particular were significant within the level of p