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Enterprise systems are commonly implemented by firms through outsourcing arrangements with software vendors. However, deriving benefits from these implementations has proved to be a challenge, and a great deal of variation has been observed in the extent of value generated for client and vendor firms. This research examines the role of co-specialization as a strategy to make the most out of outsourced enterprise systems. The authors develop hypotheses relating resource co-specialization with two indicators of success for implementation of enterprise software: (1) exchange success and (2) firm growth.

The hypotheses are tested using a unique panel data set of 175 firms adopting Advanced Planning and Scheduling (APS) software, a type of enterprise system used for managing manufacturing and logistics. The authors identify organizational factors that support co-specialization and then examine how co-specialization is associated with enterprise software implementation success, controlling for the endogenous choice to co-specialize.

The empirical results suggest that resource co-specialization is positively associated with implementation success and that the two resource co-specialization pathways that are examined complement each other in providing performance benefits.

This paper contributes to the research literature on outsourcing. The study also provides a new empirical test using a unique data set of 175 firms adopting APS Software.

This study aims to explore how a microlending digital platform connects social investors in developed countries and micro-entrepreneurs in Africa. However, additional research is necessary to discuss how online auction models are designed and implemented and how existing theories can explain their use in the so-called developing countries.

The research is based on a single case study: an online auction model for microlending named AfricaMC. Two main methods collected empirical data, namely, online participant observation, i.e. real-time participation in the online auction market and in the forum of discussions, where the authors observed the processes of microlending transactions as registered members; analysis of online documents, by reviewing forum discussions, analyzing reports, blogs, chats and other materials.

The results suggest that using sociological and information systems theoretical lenses in a complementary manner could provide greater value than using economics.

The study makes two main contributions. First, it mobilizes a pluralist theoretical approach based on economic, sociological and information systems perspectives to improve the understanding of microlending digital platforms using online auction models. Second, it uses the understanding produced from data analysis of one particular African case to validate propositions derived from these three theoretical approaches that might be applied to other cases.

Medical research requires biological material and data collected through biobanks in reliable processes with quality assurance. Medical studies based on data with unknown or questionable quality are useless or even dangerous, as evidenced by recent examples of withdrawn studies. Medical data sets consist of highly sensitive personal data, which has to be protected carefully and is available for research only after the approval of ethics committees. The purpose of this research is to propose an architecture to support researchers to efficiently and effectively identify relevant collections of material and data with documented quality for their research projects while observing strict privacy rules.

Following a design science approach, this paper develops a conceptual model for capturing and relating metadata of medical data in biobanks to support medical research.

This study describes the landscape of biobanks as federated medical data lakes such as the collections of samples and their annotations in the European federation of biobanks (Biobanking and Biomolecular Resources Research Infrastructure – European Research Infrastructure Consortium, BBMRI-ERIC) and develops a conceptual model capturing schema information with quality annotation. This paper discusses the quality dimensions for data sets for medical research in-depth and proposes representations of both the metadata and data quality documentation with the aim to support researchers to effectively and efficiently identify suitable data sets for medical studies.

This novel conceptual model for metadata for medical data lakes has a unique focus on the high privacy requirements of the data sets contained in medical data lakes and also stands out in the detailed representation of data quality and metadata quality of medical data sets.

The purpose of the paper is to propose an effective approach of artificial intelligence (AI) addressing social-humanitarian reality comprising non-formalizable representation. The new task is to describe processes of integration of AI and humans in the hybrid systems framework.

Social-humanitarian dynamics contradict traditional characteristics of AI. Suggested methodology embraces formalized and non-formalized parts as a whole. Holonic and special convergent approaches are combined to ensure purposefulness and sustainability of collective decision-making. Inverse problem solving on topology spaces, control thermodynamics and non-formalizable (considering quantum and relativistic) semantics include observers of eigenforms of reality.

Collective decision-making cannot be represented only by formal means. Thus, this paper suggests the equation of hybrid reality (HyR), which integrates formalizable and non-formalizable parts conveying and coalescing holonic approaches, thermodynamic theory, cognitive modeling and inverse problem solving. The special convergent approach makes the solution of this equation purposeful and sustainable.

The suggested approach is far reaching with respect of current state-of-the-art technology; medium-term limitations are expected in the creation of cognitive semantics.

Social-humanitarian events embrace all phenomena connected with individual and collective human behavior and decision-making. The paper will impact deeply networked experts, groups of crowds, rescue teams, researchers, professional communities, society and environment.

New possibilities for advanced AI to enable purposeful and sustainable social-humanitarian subjects. The special convergent information structuring during collective decision-making creates necessary conditions toward the goals.

This study aims to support researchers and practitioners in finding suitable instruments for future research studies and organizational quality assessments.

Employees’ success of learning at work is strongly influenced by the quality of the workplace learning environment. In the recent decades growing effort has been given to the development of surveys to measure the quality of workplace learning, resulting in a large number of available survey instruments. This study conceptually draws on a 3-P model and uses a qualitative metasynthesis to collect and categorize n = 94 surveys that intend to measure the quality of workplace learning (WPL).

The results underline that research on WPL environments is a highly interdisciplinary endeavor, where every discipline enriches the field by a new perspective and own foci. Overall, this study finds a focus on learning culture and working conditions, on social and functional inclusion of the learner and on support and feedback during training. Products of WPL such as professional competences or career aspirations play a minor role.

With the integration of quality measurement instruments from various research studies, this study produces an interactive online instrument map that gives a broad, yet organized overview of available quality measures in the WPL field.

The purpose of this paper is to present a methodology for research through game design and discuss how simulation games can be used to bridge the gap between operational exercises and simulation or analytical modelling and to provide guidelines on how simulation games can be designed for different research purposes in the context of humanitarian logistics.

This paper combines a literature review on gaming as a research method with an analysis of requirements for humanitarian logistics research methods. Starting from this theoretical framework, the authors develop a design thinking approach that highlights how games can be used for different research purposes. To illustrate the approach, the authors develop two different game set-ups that are of increasing fidelity and complexity. Finally, the authors discuss the results of the evaluation of both approaches, reflect on the design choices and provide recommendations for research and practice.

Gaming is a suitable research method to explore and analyse behaviour and decisions in emergent settings that require team work and collaborative problem solving. Especially when safety and security concerns may hinder access and experimentation on site, gaming can offer a realistic and engaging quasi-experimental environment. The aspects of engagement and realism also make gaming a suitable tool to combine training and research.

Although the use of games has attracted some attention in commercial supply chain management and crisis response, there is no systematic overview of gaming as a research method in humanitarian logistics. This paper is set to make a headway in addressing this gap by proposing a concrete approach to design games for humanitarian logistics research.

This paper investigates the structural model of vector autoregression (SVAR) of the interdependent relationship of inflation, monetary policy and Islamic banking variables (RDEP, RFIN, DEP, FIN) in Indonesia. By using monthly data for the period 2001M01-2019M12, the impulse response function (IRF), forecasting error decomposition variation (FEDV) is used to track the impact of Sharīʿah variables on inflation (prices).

This research uses quantitative approach with SVAR model to reveal the problem.

The empirical results of SVAR, the IRF show that policy shocks have a negative impact on all variables in Islamic banking except the equivalent deposit interest rate (RDEP). The impact of both conventional (7DRR) and Sharīʿah (SBIS) policies has a similar pattern. While the transmission of Sharīʿah monetary variables as a policy operational target in influencing inflation is positive. In addition, the FEDV clearly revealed that the variation in the Sharīʿah financial sector was relatively large in monetary policy shocks and their role in influencing prices.

The empirical results of SVAR, the IRF show that policy shocks have a negative impact on all variables in Islamic banking except the equivalent deposit interest rate ‘RDEP’. The impact of both conventional “7DRR” and Sharīʿah “SBIS” policies has a similar pattern. While the transmission of Sharīʿah monetary variables as a policy operational target in influencing inflation is positive. In addition, the FEDV clearly revealed that the variation in the Sharīʿah financial sector was relatively large in monetary policy shocks and their role in influencing prices.

This study analyzed three years of data from the Collegiate Leadership Competition to investigate potential differences in longitudinal leader self-efficacy growth between students who identify as men and those who identify as women.

Survey design.

Results indicate that women participants enter their competition experience at higher levels of leader self-efficacy than men and that both groups were able to sustain moderate levels of growth measured several months after the end of the competition.

The gap between men and women in their leader self-efficacy did not change over the several months of measurement. Implications for leadership educators are discussed.

Doing business in depleted contexts requires the adoption of an unconventional strategic orientation based on the involvement of the local community and driven by the attainment of economic, environmental and social goals. Previous studies have explored the specific nature of community enterprises (CEs); notwithstanding, little attention has been paid to the understanding of the strategic posture adopted by community entrepreneurs to overcome difficulties and make the business up. In this vein, the study aims to investigate how CEs operating in depleted contexts manage to survive, by successfully achieving multiple – conflicting – goals.

The authors adopted the Humane Entrepreneurship (HumEnt) framework as a form of institutional entrepreneurship where resources are leveraged to evolve the institutional context. This research adopts the case study strategy, focusing on Italian rural CEs.

The HumEnt approach, which takes into account both economic and non-economic and altruistic values of entrepreneurs, turned as better suited – compared to other approaches – to explain why people try to make business in such high-risk contexts. Second, the holistic approach of the HumEnt framework allowed catching up the particular mechanism that has enabled the CEs to obtain positive achievement.

The adoption of the HumEnt perspective enabled us to understand better the way CEs may survive and even grow where other initiatives have failed.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.