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Psychological and entrepreneurial traits have been widely studied as explicative variables of encouraging entrepreneurial behavior, while their impact on innovative activity is less explored. This study examines whether, how and why psycho-entrepreneurial traits and social networks effect innovativeness among women firm owners.

Analysis of data collected from 304 Saudi women entrepreneurs accompanied by business accelerators provides a wide support with some notable exceptions. We use Structural Equation Modeling technique to estimate how different constructs interact with each other and jointly affect directly or indirectly women's innovativeness behavior in Saudi Arabia.

Findings point out that innovativeness is positively and significantly affected by emotional intelligence, internal locus of control, entrepreneurial alertness and entrepreneurial self-efficacy. The construct of entrepreneurial self-efficacy mediates the relationship between both business and personal networks and innovativeness. However, professional forums and mentors have no significant effect on innovativeness.

The sample selection is limited to two entrepreneurial support structures especially business accelerator and business incubator. Expanding the context to other support structures can reinforce the implications and provide more valuable results.

The findings are likely to be of applicability for improving women entrepreneurship by entrepreneurial support structures.

This research is original in the sense that it investigated useful insights of innovativeness among Saudi female entrepreneurs.

The purpose of this paper is to investigate possible improvements in the pursuit of the sustainable development goals (SDGs) by multinational enterprises (MNEs) through an analysis of the literature.

A critical framework based on Gleicher’s formula for change is provided after conducting a systematic literature review.

The best way to pursue the SDGs is through an integrated approach that recognises the importance of MNEs in terms of possibilities and power of action. Working towards the SDGs appears to be largely limited by three aspects of the problem: its complexity and wickedness, the genuine interest in reaching some SDGs, at the expense of profit and low foresight.

A fundamental limitation of the study concerns, as in most of the literature on the matter, the impossibility of providing an optimal solution to the problem of meeting the SDGs, given their nature. However, formulating the best definition of the problem and its characteristics can contribute to making its management better.

This study has social implications due to the extreme importance that many SDGs have with regard to democracy and social equity, beyond their environmental and economic aspects.

The claimed contribution is the value brought by the synthesis of several points of view, through the interdisciplinary analysis of the research question. The novelty consists in organising the literature according to the formula for change.

This study aims to find how the turnover of host country nationals (HCNs) would be affected by the knowledge transfer from a headquarter to a subsidiary. Knowledge transfer in a multinational corporation (MNC) has been discussed as a critical factor in the MNC’s success. Because HCNs are essential to synergizing with a new knowledge inflow during this knowledge transfer process, their turnover entails negative consequences such as knowledge loss.

This paper empirically tests the unbalance between knowledge received (KR) and absorptive capacity (AC) as the most critical organizational predictor by using the secondary longitudinal records and survey data of 4,915 employees. Multilevel survival analysis is used to calculate the individuals’ turnover hazard.

While finding that the primary effect of transferred knowledge is to reduce turnover, the study demonstrates the unbalance between a subsidiary’s AC and KR increases the likelihood of HCNs’ turnover within the organization. The authors also recognize the possibility of nonlinear trends of KR and AC on the turnover hazard.

The authors answer how knowledge transfer shapes a subsidiary’s work environment to prevent or increase turnover, which has been barely examined for HCNs who comprise the crucial demographic group in knowledge transfer. To enhance the originality further, this study empirically observes the actual turnover of HCNs with a conceptually comprehensive view incorporating both learning and political approaches.

The rate of project failure across the globe seems to reinforce poor performance as a norm. However, engagement with construction digitalisation (CD) represents a departure point for improving project performance. Amidst researchers' recent empirical engagement with CD, the knowledge of the relationship model between CD and project delivery (PD) is abysmal. As a result, developing a business case for CD in developing countries has been slow due to the dearth of empirical evidence. This paper aims to investigate the influence of digitalisation on project performance.

Anchored on cross-sectional survey research design using a questionnaire survey in which a total of 183 copies of structured questionnaires were randomly distributed to medium- and large-sized construction firms operating in Abuja, Nigeria's federal capital. A total of 126 valid responses were received giving an overall response rate of 68.8%. The responses were analysed using mean item score, principal component analysis and multiple linear regression.

Findings from the regression analysis reveal that digitalisation has varying levels of impact on PP measured using quality, time and cost. The relationship model with time performance is weak (r = 0.526, r² = 0.277); on cost performance, the significant model is also weak (r = 0.502, r² = 0.252) and moderate on quality (r = 0.663, r² = 0.439). CD influences project cost, time and quality performance despite the weak relationship model. The results indicate that the most effective benchmark of CD is quality performance.

This study established the relationship between digitalisation and construction PD within the construction industry context, an area lacking research attention in emerging economies. This study is the first study in emerging economies that established the influence of digitalisation on construction PD statistically.

The purpose of this study is to analyze the influence of the firms' external environment on their export intensity. More specifically, it assesses whether domestic market characteristics such as domestic demand and general export environment related to tradability across borders affect firms' export intensity.

The authors use a sample of 29,266 firms from nine European countries, for the period of 2010–2016, and test several estimation methods (random effects models, Tobit models, and Heckman's selection models).

Results show that external factors such as domestic demand and ease of trade across borders are important determinants of firms' export intensity. Moreover, results reveal that firm's internal characteristics such as age, size and productivity also play an import role.

Studies about the influence of the firms' external environment on firms' export intensity are scarce because most of them are confined to a single country context. In this way, the present study contributes to the body of knowledge on the influence that external factors can have on firms' export performance by analyzing firms from nine European countries, which has important policy implications.

Optimal application and commitment toward financial management practices enhance organization performance. This study aims to assess the influence of financial management practices on organizational performance of small- and medium-scale enterprises.

Data were collected from 45 small-sized and 72 medium-sized firms. Data supported the hypothesized relationships. Construct reliability and validity were established through confirmatory factor analysis. The conceptual model and hypotheses were evaluated by using structural equation modeling.

The results indicate that working capital significantly influenced organizational performance. Capital budget management significantly influenced organizational performance. A non-significant influence of asset management on organizational performance was observed.

The generalizability of the findings will be constrained due to the research’s SMEs focus and cross-sectional data.

The study’s findings will serve as valuable pointers for stakeholders and decision-makers of SMEs in the development of well-articulated and proactive financial management systems to ensure competitiveness, sustainability, viability and financial competences.

The study adds to the corpus of literature by evidencing empirically that financial management practices significantly influenced SMEs’ performance.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.