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This study aims to examine the applicability of Hartmann et al.’s (2018) service ecosystem framework to the day-to-day management of the modern sales force. The authors provide a review of the framework, acknowledging its strengths, while also indicating areas for advancement. The authors conclude with recommendations to the framework and indicate opportunities where future research could advance sales theory.

A review of the theoretical underpinnings of the service ecosystem framework is weighed against the established roles and responsibilities of the modern sales force in the literature.

The ability of the framework to capture the multi-level, multi-actor and dynamic aspects of sales represents an improvement in the conceptualization of selling is critical. Suggestions around the refinement for meso-level sales interactions and a more pliant application of service dominant-logic are offered.

The suggested extensions of the framework continue the advancement of novel theorization for the field of sales. Priorities for future research include consideration of ethical implications of the framework and formulations of new management strategies reflective of the broad and dynamic properties of the ecosystem conceptualization.

This paper provides managerial guidelines and implications tied specifically to the thick and thin crossing points and how they may impact employee decision-making.

To the best of the authors’ knowledge, this study is the first to pointedly examine the service ecosystem framework with respect to established principles of managing a modern sales force.

The coronavirus disease 2019 (COVID-19) pandemic has had a big impact on organisations globally, leaving organisations with no choice but to adapt to the new reality of remote work to ensure business continuity. Such an unexpected reality created the conditions for testing new applications of smart home technology whilst working from home. Given the potential implications of such applications to improve the working environment, and a lack of research on that front, this paper pursued two objectives. First, the paper explored the impact of smart home applications by examining the factors that could contribute to perceived productivity and well-being whilst working from home. Second, the study investigated the role of productivity and well-being in motivating the intention of remote workers to use smart home technologies in a home-work environment in the future.

The study adopted a cross-sectional research design. For data collection, 528 smart home users working from home during the pandemic were recruited. Collected data were analysed using a structural equation modelling approach.

The results of the research confirmed that perceived productivity is dependent on service relevance, perceived usefulness, innovativeness, hedonic beliefs and control over environmental conditions. Perceived well-being correlates with task-technology fit, service relevance, perceived usefulness, perceived ease of use, attitude to smart homes, innovativeness, hedonic beliefs and control over environmental conditions. Intention to work from a smart home-office in the future is dependent on perceived well-being.

The findings of the research contribute to the organisational and smart home literature, by providing missing evidence about the implications of the application of smart home technologies for employees' perceived productivity and well-being. The paper considers the conditions that facilitate better outcomes during remote work and could potentially be used to improve the work environment in offices after the pandemic. Also, the findings inform smart home developers about the features of technology which could improve the developers' application in contexts beyond home settings.

Innovation for service contributes to service quality and customer satisfaction, and further benefits service-centered organizations to sustain competitive advantages. However, concurrent mediating and moderating mechanisms underlying innovation for service at both the group and individual levels have been scarcely investigated. The purpose of this study is to explore multilevel mediating and moderating mechanisms behind the relationship between dual-level transformational leadership (TFL) and innovation for service at the group and individual levels.

Data were collected from two countries (i.e. China and Australia). Multilevel structural equation modeling was employed to validate the research model. Bootstrapping with 5,000 replications and latent moderated structural equation modeling were used to respectively examine the mediating and moderating mechanisms.

The cross-national results showed that task interdependence and creative role identity respectively played as the group-level and individual-level mediating roles between TFL and innovation for service. It was also found that task interdependence played as a cross-level predictor enhancing individual innovation for service. Task interdependence was a moderator on the relationship between individual-level TFL and creative role identity among Australian employees, but not among Chinese employees. The relationship between creative role identity and individual innovation for service was not moderated by task interdependence among both Chinese and Australian employees.

This study contributes to advancing the TFL–innovation research through revealing dual-level TFL as the antecedent of innovation for service at both the group and individual levels. It also extends the understandings of the mediating and moderating mechanisms behind this dual-level relationship between TFL and innovation for service.

This study aims to develop and test a research model that explores the empirical relationship between consumer religiosity, brand love and consumer forgiveness. Its objective was to enhance our understanding of the mechanisms that can influence consumers to extend forgiveness to brands in the context of Islamic banking in Tanzania.

The study used a quantitative cross-sectional survey design to gather data from 399 respondents in the Dodoma and Dar-es-salaam regions of Tanzania. A structured questionnaire was used to collect the data, which were subsequently analyzed using structural equation modeling (SEM) with AMOS 21.

The study’s findings revealed that consumer forgiveness is influenced by the level of brand love at an individual level. Additionally, the findings indicate that in the context of Islamic banking, brand love is an emotional behavior that is influenced by the strength of religious beliefs, that is, consumer religiosity. Consequently, the findings highlighted the mediating role of brand love in the proposed relationship between consumer religiosity and consumer forgiveness.

The fact that Islamic banking is guided by Islamic laws (Sharia) and Islamic values means that competitiveness in this sector can be established by serving consumers who are well-versed in Islamic teachings and doctrines. Furthermore, customers who possess a strong understanding of Islamic teachings and doctrines can be an asset to Islamic banks, as they are less likely to switch banks due to service delivery issues.

This empirical study is one of the few attempts to explore the relationship between consumer religiosity, consumer forgiveness and brand love. It expands our understanding of consumer forgiveness by examining the influence of deontological norms (applying norms to assess Islamic banking practices) and teleological evaluation (evaluating Islamic banking practices based on the overall balance of right and wrong expected to occur).

The purpose of this study is to comparatively analyze the effect of dividend policy on shareholders’ wealth in Shariah-compliant (SC) and noncompliant (NC) nonfinancial firms in Pakistan.

All the nonfinancial firms listed on the Pakistan stock exchange have been taken as a sample for 2016–2021. The Karachi Meezan index screening criteria were applied to screen SC firms. Based on the BPLM and Hausman test results, the authors used the fixed-effect and pooled OLS model for SC and NC firms, respectively. The F-test was used to compare the effect of each dividend policy variable on shareholders’ wealth for both firm types.

The findings reveal that the dividend policy does affect the shareholders’ wealth in both firm types. Dividend per share (DPS), dividend yield (DY) and earnings per share significantly affect the shareholders’ wealth in SC firms. For NC firms, the dividend payout, DPS and DY are critical. Moreover, the F-test results show that the DPS, DY and leverage effect on the shareholders’ wealth significantly differ for both firm types.

This study fills the research gap in the Pakistani context specifically as well as globally by providing important insights into the relationship between a firm’s dividend policy and shareholders’ wealth for SC and NC firms. In addition, this study comprehensively compares the results for both firm types, which is also lacking in the existing literature. Because this study is based in Pakistan, the generalizability of the results would be limited.

The findings of this study are helpful for the management of SC and NC firms in devising their dividend policies that can maximize their shareholders’ wealth. This study also provides guidance and knowledge to investors in choosing companies for their investments that can maximize their wealth.

To the best of the authors’ knowledge, this is the first study that analyzes the relationship between dividend policy and shareholders’ wealth for SC firms in Pakistan. It is also the first study that comprehensively compares the dividend policy relationship with shareholders’ wealth for SC and NC firms. In addition, using the F-test for joint hypotheses to compare the specific effect of each dividend policy variable is a methodological contribution of the study.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.