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The purpose of this paper is to extend significantly the technology acceptance model (TAM) of Davis to design an extended TAM model to be used in the evaluation and assessment of e-information services for information research such as e-library services. The present TAM extension is based on two variables of behavioral intention: the perceived ease of use and perceived usefulness of a system. Furthermore, the user satisfaction, free access, information architecture, content richness, policies and rules, publishers’ quality, system self-efficacy and task technology fit were incorporated into the TAM to extend it with other factors theoretically motivated and would be of interest more generally.

The researcher adopted an experimental approach-based comparison between an experimental group (107 researchers) using an electronic information service (the e-library service of the university) and a control group (107 researchers) not registered in this e-information service of the university.

The researcher used the effect size values based t-test independent samples at the 0.05 level to adapt the structural model equation to the experimental sample. Principal results show that the behavioral intention was influenced significantly by user satisfaction. The perceived usefulness of the e-library services was influenced significantly by the perceived ease of use, information architecture, content richness, free access, publishers’ quality, task-technology fit and e-library service self-efficacy.

This paper is useful in advancing a framework for the evaluation and assessment of the electronic information service used for information research and exploring users’ attitudes toward using that service.

Artificial neural network (ANN) models, part of the discipline of machine learning and artificial intelligence, are becoming more popular in the marketing literature and in marketing practice. This paper aims to provide a series of tests between ANN models and competing predictive models.

A total of 46 pairs of models were evaluated in an objective model-building environment. Either logistic regression or multiple regression models were developed and then were compared to ANN models using the same set of input variables. Three sets of B2B data were used to test the models. Emphasis also was placed on evaluating small samples.

ANN models tend to generate model predictions that are more accurate or the same as logistic regression models. However, when ANN models are compared to multiple regression models, the results are mixed. For small sample sizes, the modeling results are the same as for larger samples.

Like all marketing research, this application is limited by the methods and the data used to conduct the research. The findings strongly suggest that, because of their predictive accuracy, ANN models will have an important role in the future of B2B marketing research and model-building applications.

ANN models should be carefully considered for potential use in marketing research and model-building applications by B2B academics and practitioners alike.

The research contributes to the B2B marketing literature by providing a more rigorous test on ANN models using B2B data than has been conducted before.

This study is an updated and enhanced version of the study named “Building a methodology to assess the e‐government transformation success” presented by İskender and Özkan in the 18th Americas Conference on Information Systems (AMCIS 2012). The main purpose of this study is to explain the development of the methodology presented in the previous study in detail, but as an add‐on to the previous one, it also applies the developed methodology on the real data collected from Turkish public institutions to show the practical implementation of it and to evaluate the preliminary results.

The methodology explained in this study is designed to collect quantitative data to assess the relationship between the success level of e‐government transformation in Turkish public institutions (dependent variable) and the probable factors (independent variables) which might be effective on this success. The dependent variable is formed by using the framework of IS Success Model created by DeLone and McLean (D&M IS Success Model), while the independent variables are gathered from literature by analyzing 100 studies focusing on probable success factors.

The major output of this study is a common methodology designed to collect and analyze the data of different stakeholder groups in e‐government transformation, but the practical implementation shows that the independent variables gathered from the literature as the probable causes of the success in e‐government transformation are not really the causes of this success, although they are positively correlated to it. In addition to this, trying to evaluate the effect of each single independent variable on the dependent variable separately is not a logical way, as some of these variables are subject to reciprocal suppression with the other ones.

Although the developed methodology is a generic one, the practical implementation of it in this study only uses the data of Turkish public institutions.

Since the methodology is a generic one, it might be used by the other interested researchers to do similar or alternative analyses with the same or different sample groups.

Understanding the relationship between the e‐government transformation success and the probable success factors better might be helpful to develop better policies focusing on the social, legal, technical and economic dimensions of the subject more properly.

Apart from the similar studies in the literature, the methodology developed in this study provides a way for the researchers who want to collect comparable quantitative data not only from external stakeholders (citizens who use e‐government services) but also from internal stakeholders (government officials who provide these services and benefit from them in the internal processes of the public institutions).

Based on the American antidumping cases against China, the purpose of this paper is to construct an early warning model for Chinese exports.

In order to overcome the drawbacks of the existing early warning models for antidumping, first, the authors screen out six most relevant indices that play a key role in US textile corporations' decision of antidumping petition against China from 2002 to 2006, then design a early warning system for antidumping petition based on panel data logit model.

The regression result indicates that unemployment ratio and import‐penetration ratio significantly influence the antidumping filing decisions; when the other invariables keep the same, with the market share of China textile goods increasing by 1 per cent point, the odds ratio of antidumping petitions against China textiles increases by about 3.7 per cent.

As far as the authors are aware there is no definite research yet about early warning system of antidumping events, and this paper aims to specifically address this issue.

This paper aims to examine the factors affecting the implementation of corporate social responsibility (CSR) sustainably in mobile operators in the kingdom of Bahrain.

The research relies on the existing literature as a secondary data source. The primary data was collected through questionnaires distributed to three leading mobile operators in the Kingdom of Bahrain. The research's population numbered 1,689, and the sample size was 313; the simple random sampling method was used for data collection with a response rate of 87.2% out of a total sample size of 273 respondents. In addition, the responses were analyzed using statistical package for the social sciences (SPSS) software version 24.0; specifically, Spearman's rank correlation was used to test the hypotheses. The research design was quantitative, so a nonparametric procedure was applied to test the hypotheses.

The research produced positive relationships between the independent variables (economic, legal, ethical, philanthropic and environmental responsibilities) and the dependent variable: CSR implementation by Bahrain mobile operators; therefore, the five proposed hypotheses were accepted; furthermore, the highest positive correlation coefficient was 0.735 for environmental responsibility, and the lowest correlation coefficient was 0.533 for economic responsibility.

The research produced positive relationships between the independent variables (economic responsibility, legal responsibility, ethical responsibility, philanthropic responsibility and environmental responsibility) and the dependent variable: CSR implementation by Bahrain mobile operators; therefore, the five proposed hypotheses were accepted; furthermore, the highest positive correlation coefficient was 0.735 for environmental responsibility, and the lowest correlation coefficient was 0.533 for economic responsibility.

The outcomes of the research mainly suggest that mobile operators assign employees who have been working with the company for more than 7 years to implement CSR; due to their engagement to implement CSR as a sustainable practice more than others with less than 3 years or 3-6 years in Bahrain mobile operators. Besides, the research provides a starting point by which other researchers could investigate CSR in other sectors in the Kingdom of Bahrain.

The research provided a framework for Bahrain mobile operators to assist them in enhancing the implementation of CSR in a sustainable manner, which are economic, legal, ethical, philanthropic and environmental responsibilities.

To compete and to survive in this era of globalization, organizations, including family businesses, need to have competitive advantage, and innovation and internationalization are some of the ways to achieve this. This paper aims to analyze whether family businesses innovate and internationalize more than non-family businesses and further analyses the type of family businesses “age-wise” and “size-wise” that innovate and internationalize more.

The study is empirical in nature. The period of study is 11 years, from 2005 to 2015 (both years inclusive). The sample is chosen from Bombay Stock Exchange (BSE) 500 index, a broad-based index in India, covering about 20 industries of the economy. The present study uses multiple regression models to find the innovativeness and internationalization of family businesses. The dependent variables are R&D (proxy for innovativeness) and FXINC (proxy for internationalization). The independent variables are FB (variable that defines whether a business is family business or non-family business); FBAGE (variable that defines the age of the family business); and FBSIZE (variable that defines the size of the family business). The other control variables used in in the study are TA (total assets), REV (revenue), CR (current ratio), QR (quick ratio), DER (debt-equity ratio) and RONW (return on net worth). Fixed effects model was used to understand the innovativeness and internationalization of family businesses. Both industry and year fixed effects were used. SPSS 20.0 version is used for the analysis. All results are heteroscedastic consistent using Breusch–Pagan test.

It is found that family businesses are more innovative and internationalized when compared to non-family businesses. The results are consistent with the resource-based theory where it is found that family businesses are entrepreneurial in nature (Salvato, 2004; Zahra et al., 2004; Kellermanns and Eddleston, 2006) which makes them more innovative. It was also found that within the family businesses, younger firms were more innovative and internationalized than older firms. This can be explained by the theory of “learning advantages of newness”, according to which younger firms are more flexible, eager to learn, have less internal resistance and are able to adapt to the changing environment much faster.

During the studies, the authors have found that there is no conclusive evidence on the innovativeness and internationalization of family businesses. Further, there are apparently negligible studies that analyze what type of family businesses, age wise (younger or older firms) and size wise (smaller or larger firms) use the strategy of innovation and internationalization to grow. The present study analyses the innovativeness and internationalization of family businesses when compared to non-family businesses and also studies the type of family businesses (age wise and size wise) that are more innovative and internationalized.

The housing market in Kenya continues to experience an excessive imbalance between supply and demand. This imbalance renders the housing market volatile, and stakeholders lose repeatedly. The purpose of the study was to forecast housing prices (HPs) in Kenya using simple and complex regression models to assess the best model for projecting the HPs in Kenya.

The study used time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited. Linear regression, multiple regression, autoregressive integrated moving average (ARIMA) and autoregressive distributed lag (ARDL) models regression techniques were used to model HPs.

The study concludes that the performance of the housing market is very sensitive to changes in the economic indicators, and therefore, the key players in the housing market should consider the performance of the economy during the project feasibility studies and appraisals. From the results, it can be deduced that complex models outperform simple models in forecasting HPs in Kenya. The vector autoregressive (VAR) model performs the best in forecasting HPs considering its lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and bias proportion coefficient. ARIMA models perform dismally in forecasting HPs, and therefore, we conclude that HP is not a self-projecting variable.

A model for projecting HPs could be a game changer if applied during the project appraisal stage by the developers and project managers. The study thoroughly compared the various regression models to ascertain the best model for forecasting the prices and revealed that complex models perform better than simple models in forecasting HPs. The study recommends a VAR model in forecasting HPs considering its lowest RMSE, MAE, MAPE and bias proportion coefficient compared to other models. The model, if used in collaboration with the already existing hedonic models, will ensure that the investments in the housing markets are well-informed, and hence, a reduction in economic losses arising from poor market forecasting techniques. However, these study findings are only applicable to the commercial housing market i.e. houses for sale and rent.

While more research has been done on HP projections, this study was based on a comparison of simple and complex regression models of projecting HPs. A total of five models were compared in the study: the simple regression model, multiple regression model, ARIMA model, ARDL model and VAR model. The findings reveal that complex models outperform simple models in projecting HPs. Nonetheless, the study also used nine macroeconomic indicators in the model-building process. Granger causality test reveals that only household income (HHI), gross domestic product, interest rate, exchange rates (EXCR) and private capital inflows have a significant effect on the changes in HPs. Nonetheless, the study adds two little-known indicators in the projection of HPs, which are the EXCR and HHI.

The purpose of this paper is to conduct a critical review of the mediation studies published in the field of Human Resource Development (HRD) to discern if the study designs, the nature of data collection and the choice of statistical methods justify the causal claims made in those studies.

This paper conducts a critical review of published refereed articles that examined mediation in Human Resource Development Quarterly, Human Resource Development International, Advances in Developing Human Resources and European Journal of Training and Development. Mediation studies published in these journals from 2000 to 2015 were identified and coded. The four journals sampled were chosen to provide breadth of coverage of the different types of empirical studies published in the field of HRD.

The review findings imply that HRD scholars are not employing experimental or longitudinal designs in their studies when randomized experiments and longitudinal studies with at least three waves of data collection are regarded as the golden standards of causal research. Further, the findings indicate that sophisticated statistical modeling approaches like structural equation modeling are widely used to examine mediation in cross-sectional studies and most importantly, a large number of such studies do not acknowledge that cross-sectional data does not allow definite causal claims.

Although the findings urge us to rethink the inferences of mediation effects reported over the past 15 years in the field of HRD, this study also serves as a guide in thinking about framing and testing causal mediation models in future HRD research and even argues for a paradigm shift from a positivist orientation to critical and postmodern perspectives that can accommodate mixed methods designs for mediation research in HRD.

This paper presents a critical review of the trends in examining mediation models in the HRD discipline, suggests best practices for researchers examining the causal process of mediation and directs readers to recent methodological articles that have discussed causal issues in mediation studies.

The purpose of this study is to determine the relationship between four independent variables educational level, longevity, type of enterprise, and gender and the dependent variable culture, as a dimension that explains organizational learning readiness in seven small‐size business enterprises.

An exploratory correlational research design was employed to survey 498 employees in seven small‐size business enterprises in the Southern and mid‐Western United States. Findings – Three pseudo‐interval variables (education, longevity, and type of enterprise) accounted for 15.3 percent of the variance in the dependent variable culture as a means to explain organizational learning readiness.

Generalizing results beyond the specific context of small‐size business enterprise may not be possible. Thus, the interpretations are limited to the seven small‐size business enterprises studied.

Certain types of enterprises form learning cultures conducive to organizational learning readiness more than do others. Also, seasoned employees may have developed a keen sense for detecting deficiencies within the learning infrastructure.

This study focused on variables not previously studied in small‐size business enterprises that explain organizational learning readiness. Thus, the results of this study will benefit the small‐size business entrepreneur interested in building a learning culture.

The purpose of this paper is to review the literature that measures the concept of firms' information environment (IE) with focus on the validity of proxies used to measure IE.

The paper reviews the IE literature using theoretically based categories and analyzes the contextual meaning and use of IE proxies. The review is based on a selection of 284 research articles from 51 journals between 2000 and 2018. A total of 37 different proxy measures of IE are found and analyzed with respect to causality based on categories and the use of IE variables as dependent, independent and control variables.

The study indicates that the IE measures are heterogeneous and there is a lack of consensus regarding their use. The different conditions used to study IE explain part, but far from all, of this heterogeneity. Furthermore, we find that the use of IE measures is only briefly discussed or motivated among the studies in the sample. These findings suggest a necessary discussion about causality in the use of IE as dependent, independent or control variables.

This study contains new and significant information on IE and IE proxy measures. It provides an extensive literature review and provides a novel typology to analyze IE.