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The purpose of this paper is to provide a comprehensive insight into the deciding factors affecting an individual’s intention to adopt mobile banking (m-banking) services in Pakistan.

A survey approach was used with a sample of 189 responses from across Pakistan. Multi-group analysis was performed in order to detect gender differences among men and women in the process of adopting m-banking.

The paper found support for the positive effect of perceived behavioral control (PBC) and attitudes (ATT) toward m-banking adoption intentions. Significant differences between men and women were found to affect subjective norms (SN) on adoption intention, even though the combined sample of men and women was insignificant. The effect of SN on m-banking adoption is stronger for women than for men. Interestingly, the paper provides contradictory findings on the role of PBC on adoption intention. The effect of PBC on m-banking adoption intention was found to be significantly stronger for men than for women.

The results present implications of consumer behavior and marketing communication for bank marketing. Although men and women do not differ in their ATT toward m-banking service adoption in general, the succinct nuance between men and women in terms of the influence of SN and PBC with adoption intention calls for a strategic reorientation of how men and women as consumers of m-banking services should be appropriately segmented, targeted and communicated. The formulation of marketing strategies to target potential consumers and to reinforce the usefulness of m-banking to existing consumers should not be “one size fits all.” The marketing of m-banking services to segments of men and women should be approached strategically in order to increase adoption rates in developing/emerging economies.

This is the first study on m-banking services adoption in Pakistan to examine the role of gender in the innovation adoption process. The differences between the two genders and the insightful results that we found in our study help shed light on the uniqueness of the context. This study is also one of the first to test a combined technology acceptance model and theory of planned behavior model in the context of m-banking adoption in a developing country using a variance-based modeling technique.

Internet of Things (IoT) is an up-and-coming conception that intends to link multiple devices with each other. The aim of this study is to provide a significant analysis of Green IoT. The IoT devices sense, gather and send out significant data from their ambiance. This exchange of huge data among billions of devices demands enormous energy. Green IoT visualizes the concept of minimizing the energy consumption of IoT devices and keeping the environment safe.

This paper attempts to analyze diverse techniques associated with energy-efficient protocols in green IoT pertaining to machine-to-machine (M2M) communication. Here, it reviews 73 research papers and states a significant analysis. Initially, the analysis focuses on different contributions related to green energy constraints, especially energy efficiency, and different hierarchical routing protocols. Moreover, the contributions of different optimization algorithms in different state-of-the-art works are also observed and reviewed. Later the performance measures computed in entire contributions along with the energy constraints are also checked to validate the effectiveness of entire contributions. As the number of contributions to energy-efficient protocols in IoT is low, the research gap will focus on the development of intelligent energy-efficient protocols to build up green IoT.

The analysis was mainly focused on the green energy constraints and the different robust protocols and also gives information on a few powerful optimization algorithms. The parameters considered by the previous research works for improving the performance were also analyzed in this paper to get an idea for future works. Finally, the paper gives some brief description of the research gaps and challenges for future consideration that helps during the development of an energy-efficient green IoT pertaining to M2M communication.

To the best of the authors’ knowledge, this is the first work that reviews 65 research papers and states the significant analysis of green IoT.

In this digital age, the rapid technological innovation and adoption, with the increasing use of big data analytics, has raised concerns about the ability of small and medium enterprises (SMEs) to sustain the competition and innovation performance (IP). To narrow the research gap, this paper investigates the role of big data analytics capability (BDAC) in moderating the relationship between digital innovation (DI) and SME innovation performance.

This research has been carried forward through a detailed theory and literature analysis. Data were analyzed through confirmatory factor analysis and structural equation models using a two-stage approach in smartPLS-4.

Results highlight that digital service capability (DSC) significantly mediates the relationship between DI and IP. Additionally, value co-creation (VCC) directly affects digital transformation (DT), while DI has a stronger effect on DSC than IP. Furthermore, BDAC significantly moderates the relation between DSC → IP and DT → IP, whereas it has a detrimental effect on the relation between DI and IP. In addition to that, VCC, DSC, DT, DI and BDAC have a direct, significant and positive effect on IP.

This research was motivated by the practical relevance of supporting SMEs in adopting DT and the resource-based view (RBV) and technology acceptance model (TAM). This study shows that all direct and indirect measures significantly affect innovation performance, including BDAC as moderator. These findings refresh the perspective on what DT, DI, VCC, DSC and BDAC can bring to a firm's innovation performance.

This paper has contributed to DT by empirically validating a theoretical argument that suggests the acceptance and adoption of new technology. This paper aims to fill theoretical gaps in understanding BDAC and DT by incorporating the RBV and TAM theories on BDAC and DT.

Social media users can now create, exchange, modify and consume socially generated experiences which can enhance social influence toward mobile banking (MB). This study aims to provide understanding of how social actor interactions through social networking platforms (SNPs) can create social influence for MB adoption and present a research framework that can help to understand which social actors have higher social influence toward MB adoption in conventional and Islamic banks.

SNP users have different levels of perceptions and experiences about the usability and credibility of MB. Therefore, their experiences are subjective realties which can generate socially constructed knowledge. To understand these subjective realties, a social constructivist approach is adopted. Data were collected from interviews with 60 individuals from diverse occupational backgrounds.

Identification element of social influence explained that the shared reviews and recommendations of opinion leaders, industry experts, celebrities and friends were highly positive for conventional banks; therefore, there is high word-of-mouth for MB of conventional banks. Internalization of social influence highlighted that people are more likely to accept the wisdom of the crowd and close friends, which can generate their engagement and connection with MB. Finally, the compliance factor of social influence explained that people can only adopt MB when they perceive high usability and credibility.

This study has provided understanding to the marketers of how social actors on SNPs can play a role in the creation, exchange, modification and consumption of socially generated influence that can impact the MB adoption intention for conventional and Islamic banks.

Although many theories and models have been presented about the marketing strategies and antecedents of MB adoption, the extensive use of SNPs has changed marketing strategies. For example, this study has found that social media users are highly influenced by the social reviews and recommendations they receive from their close friends. Therefore, socially generated influence on SNPs can create an adoption intention toward MB.