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To investigate (1) the relationship between young adults' behavioural brand loyalty (BBL) and Japanese fashion companies' financial performance (FP) and (2) FP improvement from the perspectives of social media brand engagement (BE) and loyalty programmes (LPs) by applying the complexity theory.

A mixed methodology was employed by combining qualitative and quantitative approaches to examine the prediction of outcomes by various variables in a realistic context. The integrated model associated BE and LPs with BBL and FP, which are essential for fashion companies. We selected 14 fashion brands belonging to 14 publicly traded Japanese fashion companies and surveyed 183 Japanese consumers (aged 18–25 years) who chose these brands as their favourites, engaged with the brands and participated in LPs.

The findings reveal the positive and negative effects of the variables (BE and LP) on the outcomes (short- and long-term FP). They offer marketing implications regarding brand strategy and financial improvement by considering various combinations of causal factors and complex situations, such as the fashion brands' and consumers' characteristics.

Existing empirical studies consider consumers' symmetric reactions to the benefits and losses from variables (BE, LP and BBL) but do not realistically reveal the negative and positive effects on outcomes (FP). This study addresses this gap by applying the complexity theory and offers multiple solutions to target different consumer types to predict high FP.

Social networking platforms are increasingly using the Follower Link Prediction tool in an effort to expand the number of their users. It facilitates the discovery of previously unidentified individuals and can be employed to determine the relationships among the nodes in a social network. On the other hand, social site firms use follower–followee link prediction (FFLP) to increase their user base. FFLP can help identify unfamiliar people and determine node-to-node links in a social network. Choosing the appropriate person to follow becomes crucial as the number of users increases. A hybrid model employing the Ensemble Learning algorithm for FFLP (HMELA) is proposed to advise the formation of new follower links in large networks.

HMELA includes fundamental classification techniques for treating link prediction as a binary classification problem. The data sets are represented using a variety of machine-learning-friendly hybrid graph features. The HMELA is evaluated using six real-world social network data sets.

The first set of experiments used exploratory data analysis on a di-graph to produce a balanced matrix. The second set of experiments compared the benchmark and hybrid features on data sets. This was followed by using benchmark classifiers and ensemble learning methods. The experiments show that the proposed (HMELA) method predicts missing links better than other methods.

A hybrid suggested model for link prediction is proposed in this paper. The suggested HMELA model makes use of AUC scores to predict new future links. The proposed approach facilitates comprehension and insight into the domain of link prediction. This work is almost entirely aimed at academics, practitioners, and those involved in the field of social networks, etc. Also, the model is quite effective in the field of product recommendation and in recommending a new friend and user on social networks.

The outcome on six benchmark data sets revealed that when the HMELA strategy had been applied to all of the selected data sets, the area under the curve (AUC) scores were greater than when individual techniques were applied to the same data sets. Using the HMELA technique, the maximum AUC score in the Facebook data set has been increased by 10.3 per cent from 0.8449 to 0.9479. There has also been an 8.53 per cent increase in the accuracy of the Net Science, Karate Club and USAir databases. As a result, the HMELA strategy outperforms every other strategy tested in the study.

The purpose of this paper is to investigate the performance implications of internationalization strategies for Chinese multinational corporations (MNCs). Specifically, the authors examine the individual and joint effects of speed of internationalization in developed and developing countries and age on Chinese MNCs’ performance.

The authors constructed a unique and comprehensive database on the internationalization strategies of 206 Chinese firms over 14 years and deployed random-effect regressions to assess the effects of age, speed of expansion in terms of number of subsidiaries and countries, and the types of destination (developed vs developing country) on firm performance.

The analyses show that age is negatively related to performance but rapid expansion of subsidiaries in developing countries and geographic scope in developed countries are positively related to performance. In addition, the impacts of young age and two types of expansion, fast expansion of subsidiaries in developing countries and fast expansion of geographic scope in developed countries, are cumulative.

The authors combine the arguments of the learning advantages of newness and fast movers and model the simultaneous effects of age and speed of two types of international expansion (in terms of number of subsidiaries and countries) in both developed and developing countries on performance. The strong empirical support for the hypotheses based on analyses of a unique data set of Chinese MNCs’ internationalization patterns lends credence to the proposed model.