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This study aims to identify the role of top management team (TMT) in the internalization process and vital characteristics for successful internationalization. The literature analysis presents the evolution of TMT concept, its relatedness to internalization conditioned by resource dependency theory and institutional theory and current studies on TMT characteristics linked to internationalization. Conducted research also let us to identify which abilities, skills, or knowledge TMTs are missing, aiming to achieve better international performance. The study contributes to current small and medium-sized enterprises (SMEs) internationalization studies by providing insights, a deeper analysis of such phenomena and future research directions. Consequently, the study provides directions for SMEs, seeking to improve their performance of international activities. The practical implications of the study’s results are related to the selection and recruitment of new TMT members or recommendations for training. The evaluation of candidate’s experience, personal qualities, and communication skills are important in hiring process of managers. It might require the improvement in selection process by including business case simulation, trial day, or references from previous employers. TMT members’ capabilities should be regularly evaluated by peers and if necessary specialized training should be provided on time.

This research seeks to understand how shareholder constituencies including controlling family, nonfamily insiders, as well as domestic and foreign institutions in the corporate governance system of emerging economy firms perceive institutional risks in terms of regulative, normative, and cognitive institutions and influence strategic choices in the internationalization of their invested firms.

The sample data are Taiwanese publicly listed companies in the electronics and computer industry. Panel data of the parent firms and their overseas affiliates are available from the annual report and Taiwan Economic Journal database. Country-level data are available from the World Investment Report and the IMD World Competitiveness Report. Statistical regression models including tobit and logistic regression are used to analyze the data.

Controlling family and nonfamily insider shareholders tend to influence their invested firms to enter in institutionally smaller host countries through a shared ownership. Domestic institutional shareholders tend to influence their invested firms to adopt a shared ownership and enter in host countries with larger and smaller institutional distances in terms of regulative and normative institution, respectively. Foreign institutional shareholders tend to influence their invested firms to enter in institutionally smaller host countries through a whole ownership.

The strategic choices of foreign market entry made by emerging economy firms are significantly shaped by the different risk perceptions of shareholder constituencies in their corporate governance system toward the institutional distances between the home and the host country.

Introduction: The insurance industry is one of the lucrative sectors of the economy. However, it is volatile because of the large chunk of data generated by the transactions taking place daily. However, every bit of it is responsible for creating market trends for stock investors to predict the returns. The specialised data mining techniques act as a solution for decision-making, reducing uncertainty in decision-making.

Purpose: There are limited studies that have examined the efficiency and effectiveness of data mining techniques across the companies in the insurance industry to date. To enable the companies to take exact benefit of data mining techniques in insurance, the present study will focus on investigating the efficiency of artificial neural network (ANN) and support vector machine SVM across insurance companies of CNX 500.

Method: For predictive models, various technical indicators were considered independent variables, and change in return, i.e. increase and decrease, was deemed a dependent variable. The indicators were transformed from daily raw data of insurance company’s stock values spanning four years. We formed 90 data sets of varied periods for building the model – specifically six months, one year, two years, and four years for selected six insurance companies.

Findings: The study’s findings revealed that ANN performed best for the ICICIPRULI data model in terms of hit ratio. Whereas the performance of SVM was observed to be the best for the ICICIGI data model. In the case of pairwise comparison among the six selected Indian insurance companies from CNX 500, the extracted data evaluated and concluded that there were eight significantly different pairs based on hit ratio in the case of ANN models and nine significantly different pairs based on hit ratio for SVM models.

Consumer-generated online product reviews (OPRs) have become a crucial source of information for consumers; however, OPRs are increasingly being incentivized. The purpose of this paper is to find a method of sponsorship and disclosure that could be considered ethically sound.

This study adopted a quasi-experimental approach to clarifying how the method of sponsorship impacts reader perceptions of OPRs in terms of helpfulness, credibility and purchase intention. Two experiments were performed on an online platform using data from 480 participants. Hypotheses were tested using analysis of covariance.

Meaning under the premise that sponsorship information is disclosed and not withheld from the readers, Study 1 revealed that experiential sponsorship is the best sponsorship. Study 2 revealed that featuring reviewers with greater influence in the online community increases the positive influence of disclosing experiential sponsorship on OPR persuasiveness.

The findings in this study provide rational incentives for firms to disclose sponsorship information, i.e. demonstrate high ethical standards in marketing. This was shown to create a win-win-win situation for consumers, firms and reviewers. Managerial implications for online marketing managers are also discussed.

The purpose of this paper is to present a review of the literature on two lines of research, corporate governance and innovation, explaining how different internal corporate governance mechanisms may be determinants of business innovation.

It explores the theoretical background and the empirical evidence regarding the influence of both ownership structure and the board of directors on company innovation. Then, conclusions are drawn and possible future research lines are presented.

No consensus was observed regarding the relation between corporate governance and innovation, with both positive and negative arguments being found, and with empirical evidence not always pointing in the same direction. Thus, new studies trying to clarify this relationship are needed.

Over recent years, interest has grown in the influence of governance mechanisms on innovation decisions taken by the management. Innovation efforts and results depend on factors that are influenced by corporate governance, such as ownership structure or the functioning of the board of directors. Thus, the paper shows an updated state of the art in this field proposing future lines for empirical research.

This paper develops a generalizable, machine-learning-based method for measuring established marketing constructs using passive analysis of consumer-generated textual data from service reviews. The method is demonstrated using topic and sentiment analysis along dimensions of an existing scale: lodging quality index (LQI).

The method induces numerical scale ratings from text-based data such as consumer reviews. This is accomplished by automatically developing a dictionary from words within a set of existing scale items, rather a more manual process. This dictionary is used to analyze textual consumer review data, inducing topic and sentiment along various dimensions. Data produced is equivalent with Likert scores.

Paired t-tests reveal that the text analysis technique the authors develop produces data that is equivalent to Likert data from the same individual. Results from the authors’ second study apply the method to real-world consumer hotel reviews.

Results demonstrate a novel means of using natural language processing in a way to complement or replace traditional survey methods. The approach the authors outline unlocks the ability to rapidly and efficiently analyze text in terms of any existing scale without the need to first manually develop a dictionary.

The technique makes a methodological contribution by outlining a new means of generating scale-equivalent data from text alone. The method has the potential to both unlock entirely new sources of data and potentially change how service satisfaction is assessed and opens the door for analysis of text in terms of a wider range of constructs.

Based on the literature on information security (InfoSec) education and uses and gratifications theory, the purpose of this paper is to propose and test a research model to examine the impact of InfoSec education on social media usage.

The authors employed structural equation modeling to test the research model, with a survey data set of 293 valid subjects from a WeChat subscription about InfoSec education named secrecy view.

The results reveal the significant impacts of perceived content quality, perceived social influence and perceived entertainment on user satisfaction in the context of security education and social media. User satisfaction is significantly associated with user stickiness and security knowledge improvement. Additionally, the authors found that user’s security awareness moderated the effect of perceived entertainment on user satisfaction.

Using a single sample might constrain the contributions of this study.

The authors suggest practical guidelines for InfoSec education on social media by enhancing perceived content quality. Moreover, due to diverse user attributes, the social media operators should recommend targeted content to different users.

This study contributes to studies on InfoSec education of social media usage and identifies factors that affect user satisfaction with social media. Furthermore, the study enriches the security education practices by uncovering differences in security awareness with regard to user satisfaction.

The purpose of this paper is to provide a comprehensive review on electronic word-of-mouth (eWOM) credibility. Further, the authors propose a comprehensive and integrated model on eWOM credibility.

The authors conducted a systematic review of the extant literature on marketing, sociology and psychology to identify the factors that affect eWOM credibility. Further, the authors developed themes and identified factors which lead to eWOM credibility.

Four factors were identified, i.e. content, communicator, context and consumer, which affect eWOM credibility. Several variables associated with these four factors were identified, which result in eWOM credibility. Further, the authors developed 22 propositions to explain the causal relationship between these variables and eWOM credibility.

The conceptual model needs empirical validation across various eWOM platforms, i.e. social networking websites, e-commerce websites, etc.

Managers and e-commerce vendors can use these inputs to develop specific design elements and assessment tools which can help consumers to identify credible eWOM messages. Credible eWOM messages, in turn, will increase the “trust” and “loyalty” of the customers on e-commerce vendors.

This paper provides a conclusive takeaway of eWOM credibility literature by integrating multiple perspectives and arguments from the extant literature. This study also presents an integrated model, which provides a theoretical framework for researchers to further examine the interaction effect of various variables, which results in eWOM credibility.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2020-0263

This chapter strives to evaluate the impact of tourist arrival on energy consumption, air pollution, gross domestic product (GDP), and foreign direct investment to suggest strategies for further tourism development. Relevant data from Taiwan are analyzed, entailing tourist arrivals, GDP, carbon dioxide emissions, energy consumption, and capital investment. It tests four hypotheses using the Augmented Dickey-Fuller single root test, the Autoregressive Distributed Lag model, and time series econometrics of Granger causality. This study finds that tourist arrival is positively related to energy consumption and GDP, whereas it negatively relates to carbon dioxide emission and capital investment. In consideration of a negative relationship between tourist arrival and direct investment, this study suggests devising timely research agendas on carrying capacity and service quality in the mind of international tourists to see if additional investment in tourism infrastructures is needed.

In this study, the performance of ordinal GA-based trading strategies is evaluated under six classes of time series model, namely, the linear ARMA model, the bilinear model, the ARCH model, the GARCH model, the threshold model and the chaotic model. The performance criteria employed are the winning probability, accumulated returns, Sharpe ratio and luck coefficient. Asymptotic test statistics for these criteria are derived. The hypothesis as to the superiority of GA over a benchmark, say, buy-and-hold, can then be tested using Monte Carlo simulation. From this rigorously-established evaluation process, we find that simple genetic algorithms can work very well in linear stochastic environments, and that they also work very well in nonlinear deterministic (chaotic) environments. However, they may perform much worse in pure nonlinear stochastic cases. These results shed light on the superior performance of GA when it is applied to the two tick-by-tick time series of foreign exchange rates: EUR/USD and USD/JPY.