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The first part of this paper pointed out that theory and research on followership is less extensive and less well known than that on leadership. It then described a three factor model of leadership and suggested it could be applied to and was consistent with other work on followership. The second part of the paper presented empirical evidence supporting the three factor model of leadership and justifying its extension and application to the full range of team roles, including follower and co-worker roles, as well as leader roles. This part of the paper looks specifically at follower roles and followership. Research findings are used to develop and describe a three factor model of follower behaviour. The paper aims to discuss these issues.

A mixed group of managers, mainly from the UK public sector, completed a variety of self-assessment questionnaires, had 360 degree assessments completed on them, and provided information on their work role and situation. Research looked at the degree of correlation between variables and its statistical significance. This was used to assess the internal reliability and external validity of three factor measures of leader behaviours and team role behaviours. Information on contextual variables was used to measure leader and follower situations and develop leader-follower scales that were used to identify behaviours used by followers. In total, 360 degree assessments were also used to identify behaviours that are most and least valued when used by followers.

The findings presented in the second part of this paper provided empirical support for the three factor model of leadership and its extension to the full range of team roles. The findings presented in this part of the paper identify behaviours used by individuals in follower roles and behaviours valued when used by individuals in such roles. By combining these two sets of findings, it was possible to produce a three factor model of effective follower behaviour, with each metacategory consisting of five behaviour sets and each set made up of four specific behaviours.

Effective organisations need effective followers and effective leaders. Moreover, the skills of the effective leader develop out of and build on those of the effective follower. Effective leaders and followers use essentially the same skills but use them in different situations, playing different roles. The research was carried out on a diverse sample of managers, drawn mainly from the UK public sector. However, it would be useful to extend the research to other populations.

The findings provide evidence-based descriptions of effective follower behaviours. These have practical implications for leaders and for followers, as well those involved in their training and development. They establish the content of developmental activities for effective followers and indicate how the training and development of followers underpins that of leaders.

The findings challenge the widely held pre-occupation with leadership and the associated view that it is qualitatively different from and superior to leadership. In so doing, the three factor model of followership offers a challenge to the cult of leadership.

This is the first published research to present empirical evidence supporting the three factor model of followership. In the research process, scales were developed to assess leader and follower roles and used to identify behaviours used by followers. They were also used in further research identifying behaviours most and least valued when used by followers. The instruments and the associated research were original.

The research examines how behavioral intentions, as a higher-order construct, indirectly affect financial inclusion through service trust, usage behavior and financial literacy in mobile money adoption.

Following the positivist research philosophy, a cross-sectional study design was used to collect data through questionnaires comprised of scales adapted from prior studies. With a usable sample size of 340 respondents, this study employs partial least squares structural equation modeling to assess the model.

The study revealed the significant indirect role of behavioral intention on financial inclusion through use behavior, behavioral intentions on use behavior through service trust, and use behavior on financial inclusion through financial literacy. The role of behavioral intentions on financial inclusion through serial mediation of service trust, use behavior and financial literacy was also found to be significant.

This study's novelty resides in examining the indirect relationship between behavioral intentions and financial inclusion, specifically via the serial mediation of service trust, use behavior and financial literacy.

The concept and practice of e-services has become essential in business transactions. Yet there are still many organizations that have not developed e-services optimally. This is especially relevant in the context of Indonesian Airline companies. Therefore, many airline customers in Indonesia are still in doubt about it, or even do not use it. To fill this gap, this study attempts to develop a model for e-services adoption and empirically examines the factors influencing the airlines customers in Indonesia in using e-services offered by the Indonesian airline companies. Taking six Indonesian airline companies as a case example, the study investigated the antecedents of e-services usage of Indonesian airlines. This study further examined the impacts of motivation on customers in using e-services in the Indonesian context. Another important aim of this study was to investigate how ages, experiences and geographical areas moderate effects of e-services usage.

The study adopts a positivist research paradigm with a two-phase sequential mixed method design involving qualitative and quantitative approaches. An initial research model was first developed based on an extensive literature review, by combining acceptance and use of information technology theories, expectancy theory and the inter-organizational system motivation models. A qualitative field study via semi-structured interviews was then conducted to explore the present state among 15 respondents. The results of the interviews were analysed using content analysis yielding the final model of e-services usage. Eighteen antecedent factors hypotheses and three moderating factors hypotheses and 52-item questionnaire were developed. A focus group discussion of five respondents and a pilot study of 59 respondents resulted in final version of the questionnaire.

In the second phase, the main survey was conducted nationally to collect the research data among Indonesian airline customers who had already used Indonesian airline e-services. A total of 819 valid questionnaires were obtained. The data was then analysed using a partial least square (PLS) based structural equation modelling (SEM) technique to produce the contributions of links in the e-services model (22% of all the variances in e-services usage, 37.8% in intention to use, 46.6% in motivation, 39.2% in outcome expectancy, and 37.7% in effort expectancy). Meanwhile, path coefficients and t-values demonstrated various different influences of antecedent factors towards e-services usage. Additionally, a multi-group analysis based on PLS is employed with mixed results. In the final findings, 14 hypotheses were supported and 7 hypotheses were not supported.

The major findings of this study have confirmed that motivation has the strongest contribution in e-services usage. In addition, motivation affects e-services usage both directly and indirectly through intention-to-use. This study provides contributions to the existing knowledge of e-services models, and practical applications of IT usage. Most importantly, an understanding of antecedents of e-services adoption will provide guidelines for stakeholders in developing better e-services and strategies in order to promote and encourage more customers to use e-services. Finally, the accomplishment of this study can be expanded through possible adaptations in other industries and other geographical contexts.

The study aims to investigate the presence of herding behavior in the stock market of UK with a special emphasis on news sentiment regarding the economy. The authors focus on the news sentiment because in the current digital era, investors take their decision making on the basis of current trends projected by news and media platforms.

For empirical modeling, the authors use machine learning models to investigate the presence of herding behavior in UK stock market for the period starting from 2006 to 2021. The authors use support vector regression, single layer neural network and multilayer neural network models to predict the herding behavior in the stock market of the UK. The authors estimate the herding coefficients using all the models and compare the findings with the linear regression model.

The results show a strong evidence of herding behavior in the stock market of the UK during different time regimes. Furthermore, when the authors incorporate the economic uncertainty news sentiment in the model, the results show a significant improvement. The results of support vector regression, single layer perceptron and multilayer perceptron model show the evidence of herding behavior in UK stock market during global financial crises of 2007–08 and COVID’19 period. In addition, the authors compare the findings with the linear regression which provides no evidence of herding behavior in all the regimes except COVID’19. The results also provide deep insights for both individual investors and policy makers to construct efficient portfolios and avoid market crashes, respectively.

In the existing literature of herding behavior, news sentiment regarding economic uncertainty has not been used before. However, in the present era this parameter is quite critical in context of market anomalies hence and needs to be investigated. In addition, the literature exhibits varying results about the existence of herding behavior when different methodologies are used. In this context, the use of machine learning models is quite rare in the herding literature. The machine learning models are quite robust and provide accurate results. Therefore, this research study uses three different models, i.e. single layer perceptron model, multilayer perceptron model and support vector regression model to investigate the herding behavior in the stock market of the UK. A comparative analysis is also presented among the results of all the models. The study sheds light on the importance of economic uncertainty news sentiment to predict the herding behavior.

The purpose of this paper is to explore technology acceptance and use behavior of IS innovations by entrepreneurs. To measure the perception of IS innovations by entrepreneurs the authors review unified theory of acceptance and use of technology and the entrepreneurial potential model, empirically compare the two models, develop a new model that integrates elements from the two models, and then empirically validate the new model (technology adoption decision and use (TADU)) in a technology acceptance context.

The data used to test the hypothesis are collected from 1,200 entrepreneurs in Malaysia. The research model was analyzed using structural equation modeling.

The results indicate that perceived desirability and perceived feasibility have significant effects on entrepreneurs’ intention to adopt and use innovations. Propensity to use is an important factor that has a significant effect on individual behavior. The precipitating events that happen in the time lag between intention and behavior will disrupt entrepreneurs’ inertia and induce a change in their behavior, encouraging them to seek the best opportunity available.

Understanding the individual, technological, and environmental factors that significantly affect IT adoption behavior can support policy makers in providing guidance on the adoption and usage of IT innovations by entrepreneurs.

This study proposes a TADU model with six core determinants of intention and usage – perceived desirability, perceived feasibility, performance expectancy, effort expectancy, social influence and facilitating conditions and two new moderators, precipitating events and the propensity to act.

For a good number of Indians, their smartphone is their first digital computing device. They have less experience in dealing with the Internet-enabled device and hence less experience in handling security threats like malware as compared to users of other countries who have gone through the learning curve of handling such security threats using other Internet-enabled devices such as laptop and desktop. Because of this, the inexperienced Indian smartphone user may be vulnerable to Internet-related security breaches, as compared to the citizens of developed economies. Hence, it is essential to understand the attitude, behaviour and security practices of smartphone users in India. Limited research is available about the security behaviour of smartphone users in India as the majority of research in this domain is done outside India.

In this empirical study, the researchers identified 28 cybersecurity behaviours and practices through a survey of relevant literature. An online survey of identified cybersecurity behaviours and practices was administered to 300 smartphone users. Frequency analysis of the respondent data was done to understand the adoption of recommended cybersecurity behaviours and practices. Pearson’s chi-square with 5% level of significance has been used to test the hypotheses. Post hoc analysis with Bonferroni correction was conducted for statistically significant associations.

Overall, the respondents did not exhibit good cybersecurity behaviour. Respondents have adopted some of the most popular security features of the smartphone such as the use of screen lock. However, respondents have not adopted or are not aware of the technical security controls such as encryption and remote wipe. Statistically significant differences were found between the cybersecurity behaviour and practices and independent variables such as gender, age, mobile operating system (OS) and mother tongue. Respondents reported high level of motivation to protect their device and data, whereas they reported moderate level of threat awareness and the ability to protect to their device and data. Results of the comparative analysis with a similar study in China and the USA are also reported in this study.

The main limitations of this study are as follows: the respondents' perceptions about their cybersecurity behaviours and practices were measured as opposed to their actual behaviours and practices and the generalizability of the study is limited because the sample size is small as compared to the total number of smartphone users in India.

The findings of this study may be useful for the design of effective cybersecurity prevention and intervention programs for general smartphone users of India.

This study provides an insight about cybersecurity behaviour of smartphone users in India. To the knowledge of the researchers, this is the first study to collect such quantitative data of smartphone users in India for a better understanding of the cybersecurity behaviours and practices. This study identified 28 cybersecurity behaviours and practices, which smartphone users should follow to improve cybersecurity.

There were numerous models that were developed with the explicit purpose of predicting the information system (IS) adoption behaviour. However, these models are criticized for the fact that they do not accurately capture the effect of external factors (unforeseen events) on individual intention, which may inhibit the act of adopting and using IS. The purpose of this paper is to investigate these external factors among entrepreneur(s) and measure how the individual's propensity to use affects the adoption and use of IS innovation. This paper will also attempt to investigate the key determinants of the adoption of IT innovation among entrepreneurs in Klang Valley, Malaysia.

Based on the research model, a questionnaire was developed, and data collected from entrepreneurs based in Klang Valley Malaysia. In all, 326 responses were collected, and the research model was applied using the structural equation modelling technique.

The performance expectancy and the propensity to act are salient determinants of behavioural intention to adopt and use IS innovation. An individual's pervasive decision to use IS innovations are strong factor that influences individual intention and behaviour prediction. Furthermore, precipitating events were determined to be the underlying factor that influences the behavioural intention for an adopter to actually adopt IS innovation(s). The finding(s) demonstrates that the probability of an intention actually translating into actual usage is higher if the precipitating events occur in their respective businesses.

It is important to evaluate this study's results and contributions in light of its limitations. This is an initial test for this model, and it should be subjected to further testing and refinement, particularly with regards to the precipitating events construct. One should be aware that an entrepreneur is exposed to various events and challenges that could involve their personal, family, career, and business life. Certain events related to these factors would impact the entrepreneurs differently, which might require different coping mechanisms. Future research should focus on the precipitating events related to their personal, family, career and business life, and which of these precipitating events are most prominently affecting their decision to actively use IS innovation.

End-users, particularly, as this study reveals, do prefer new IS innovation, but they may not use it as often. Thus, developers and or inventors of IS innovation need to understand factors that can influence users’ decision to adopt and use IS innovation. As for entrepreneurs, understanding factors that influence them to use IS innovation would enhance their sense on the relative importance of using the system. Furthermore, this study provides evidence concerning the role of precipitating events, which encourages or inhibits entrepreneurs towards IS related innovation adoption behaviour. This study makes clear that government policy and environmental factors, as well as work situation significantly influences an entrepreneur's intention in using IS innovation.

This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT). Some aspects of the Entrepreneurial Potential Model are incorporated into UTAUT, with the express purposes of capturing external variables. Additionally, the role of the volitional aspect of an entrepreneur's intention towards IS innovation are also taken into due consideration. This model assist the researchers in measuring the effect of pervasive decision and external factors on technology acceptance, and will provide essential information that explains the promotion of IS innovation adoption, usage and hindrance.

In response to calls for theory use and a more reflexive turn in social marketing, this paper aims to draw on previously executed studies. In line with dominant social marketing downstream-focussed practice, the explanatory power of a commonly used theory, namely, the theory of planned behaviour (TPB), was empirically examined across three different contexts and critically assessed to guide future research practice.

TPB measures were drawn from prior studies, and inconsistent item use across contexts was observed. Quantitative studies involving from 876 to 3,191 respondents underpin this study. Each study focussed on a different behaviour, namely, walking to and from school, binge drinking and packing fruits and vegetables into lunchboxes. Hierarchical multiple linear regressions were used for data analysis.

Item use was mixed, construct reliability was not consistent and consequent findings indicated that TPB explained walking to and from school and binge drinking, but it did not explain packing fruits and vegetables into lunchboxes.

Theory use is recommended to enhance intervention outcomes. However, theory application remains scarce in social marketing. Moreover, when theory is used, consistent measures are not used; items are removed from constructs to obtain model fit and constructs used within the theory differ. The current study draws from three studies, all of which applied TPB to explain behaviours. Mixed outcomes were observed when the same analytical process was applied using the available measures and constructs. Close investigation of the measures used across the three studies highlights one explanation for mixed findings. In the absence of consistent application of the theory, drawing definitive conclusions about a theory’s effectiveness is premature. Precise application of theoretical constructs is needed to deliver theoretically derived understanding.