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Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

Firms increasingly rely on both external and internal crowdsourcing to capture ideas more broadly and enhance innovative problem-solving. Especially in internal crowdsourcing, knowledge sharing that contributes to develop or further the understanding of the problem the idea is focused on solving can take place between critical employees, and in that way improve ideas generated by others. This far, most crowdsourcing practices have focused on identifying solutions to proposed problems, whereas much less is known about how crowds can be used to share problem-related knowledge. There is thus an untapped potential in leveraging crowds not just to generate solution-oriented ideas but also to share knowledge to improve ideas and even to reframe problems. This paper aims to explore the effect of problem- and solution-related knowledge sharing in internal crowdsourcing for idea development.

Data on ideas and comments were collected from an idea management system in a Swedish multinational company. The investigation captures the influences of the problem- and solution-related knowledge sharing on ideas based on content analysis and logistic regression analysis.

The results from this study show that sharing knowledge related to solutions in idea development impacts idea acceptance positively, whereas sharing knowledge related to problems in idea development has a negative effect on the likelihood of idea acceptance and these effects of knowledge sharing are moderated by the active author responses.

This research provides managerial implications for firms to deliberately manage knowledge sharing in peer communities in internal crowdsourcing, especially by providing suggestions on problem reframing and solution refining for ideas.

The results contribute to existing theory in terms of extending the view of crowdsourcing in ideation to include how crowds contribute to the development of the problem and the solution during the development of ideas and providing new insights on knowledge sharing in internal crowdsourcing based on problem-solving theory.

People seem to function according to different models, which implies that in business and social sciences, heterogeneity is a rule rather than an exception. Researchers can investigate such heterogeneity through multigroup analysis (MGA). In the context of partial least squares path modeling (PLS-PM), MGA is currently applied to perform multiple comparisons of parameters across groups. However, this approach has significant drawbacks: first, the whole model is not considered when comparing groups, and second, the family-wise error rate is higher than the predefined significance level when the groups are indeed homogenous, leading to incorrect conclusions. Against this background, the purpose of this paper is to present and validate new MGA tests, which are applicable in the context of PLS-PM, and to compare their efficacy to existing approaches.

The authors propose two tests that adopt the squared Euclidean distance and the geodesic distance to compare the model-implied indicator correlation matrix across groups. The authors employ permutation to obtain the corresponding reference distribution to draw statistical inference about group differences. A Monte Carlo simulation provides insights into the sensitivity and specificity of both permutation tests and their performance, in comparison to existing approaches.

Both proposed tests provide a considerable degree of statistical power. However, the test based on the geodesic distance outperforms the test based on the squared Euclidean distance in this regard. Moreover, both proposed tests lead to rejection rates close to the predefined significance level in the case of no group differences. Hence, our proposed tests are more reliable than an uncontrolled repeated comparison approach.

Current guidelines on MGA in the context of PLS-PM should be extended by applying the proposed tests in an early phase of the analysis. Beyond our initial insights, more research is required to assess the performance of the proposed tests in different situations.

This paper contributes to the existing PLS-PM literature by proposing two new tests to assess multigroup differences. For the first time, this allows researchers to statistically compare a whole model across groups by applying a single statistical test.

The paper investigates the market performance of strategic acquisitions for growth in the fifth and sixth merger waves and outlines the major determinants that affect the performance of acquiring companies in these most complex and most challenging corporate transactions.

To perform the quantitative analysis a unique data sample was built out of acquisitions performed in the 5th and 6th merger waves with an only single purpose – strategic growth. Their performance was first analyzed using the method of market-based event study. In addition, the impact of several non-accounting determinants identified through a thorough literature review was tested using univariate/multivariate regression analysis.

The new findings of the study state that strategic acquisitions for growth created more value for acquiring companies if they were completed internationally and involved an acquisition of a middle-sized company. Moreover, the acquisition of targets in the less related industries (2-SIC) led to stronger performance of acquirers, especially in the international settings.

The study suggests additional directions for future research. The future analysis can investigate the post-merger acquisition performance of strategic acquirers and can focus on additional financial (accounting) determinants in the evaluation of performance. This perspective can not only address the limitations imposed by the assumption of efficient capital markets but also provide additional insights.

The results of current study have important implications for executives performing M&A for growth. They show that the market reaction to M&A announcement can be at least partially anticipated and help managers to plan their strategic moves based on a defined set of variables.

The study contributes to the sustainable, value-creating growth dynamics and encourages Executives to “lead for value.”

(1) In contrast to the existing studies that do not differentiate between the transaction rationale in their analysis, this paper focuses explicitly only on those acquisitions that have strategic growth as their primary objective and responses therefore, to the problem stated by Halpern (1983). This approach helps to mitigate the distortion of results and make a reliable assessment of the strategic move. (2) The results of quantitative analysis also outline that acquisition of mid-sized targets and larger degree of diversification (2-SIC, international focus) code were associated with higher value creation.

本文旨在研究於第五和第六波的併購浪潮中為增長而作出的策略性收購的市場表現;本文亦概述在這些極其複雜的和極具挑戰性的公司交易中影響作收購公司的表現的主要因素.

為了能進行定量分析,研究人員收集在第五及第六波的併購浪潮中以策略性增長為唯一目的的收購個案、建立一個獨特的數據樣本。研究人員首先以基於市場的事件研究法分析那些進行了收購的公司的表現,並以單變量/多變項迴歸分析法去試驗那幾個透過深入的文獻研究而找到的非會計的決定因素的影響.

研究得出的新發現是、如果以增長為目的的策略性收購是於國際間完成及涉及收購中型公司的話,則這收購行動會給進行收購的公司帶來更多價值。而且、如果收購目標的產業與作收購公司的不太相關的話 (2-標準產業分類),收購行動會為進行收購的公司帶來更強的表現、特別是在國際環境下進行這收購行動.

(1) 有別於現時其它於其分析中不區分交易理由的研究，本文明確地表示只集中探討那些以帶來策略性增長為主要目標的收購；因此、本研究對 (哈爾彭，1983年)(Halpern, 1983) 陳述的問題作出了回應。本研究的理念有助於減輕我們對收購結果的曲解，從而讓我們對策略性行動能作出可靠的評估. (2) 定量分析的結果、亦概述了以中型公司為目標的收購及更大程度的多樣化 (2－標準產業分類、以國際為焦點) 代碼與創造更大價值是有關聯的。

本研究的結果對學術界及管理人員均具吸引力，亦為策略規劃提供一個額外的工具.

本研究可帶來可持續的及可創造價值的增長動力，又可鼓勵行政主管採用以價值為本的領導方針.

The aim of this study is to conduct a comparative analysis between Islamic and conventional banks in terms of whether Islamic banks was more or less resilient/risky than conventional counterparts to the pandemic shock. It also examines the role of capital in improving the performance and stability within the two banking systems.

This study uses 82 banks from MENA (Middle East and North Africa) region for periods across 2011–2020, and employs a dynamic panel data approach to examine the resilience within both banking systems during the Covid-19 pandemic.

The results show that the Covid-19 pandemic has a negative impact on conventional banks' stability. However, Islamic banks performed better and were less risky than conventional ones. Banks with high-quality capital are more effective at controlling their risks and improving their performance during the pandemic.

The results offer important financial observations and policy implications to many stakeholders engaging with banks. Actually, the findings of this study facilitate to the stakeholders and bankers to have an alluded picture about determinants of risk and performance. The results can be used by bankers’ policy decision-makers to improve and enhance their consideration for risk management, taking into consideration the type of banking systems.

Compared to the various studies on the stability of Islamic and conventional banks, researchers have not sufficiently addressed the effect of the Covid-19 pandemic on risk and performance. Moreover, none of these studies has examined if Islamic banks was more or less resilient/risky than conventional counterparts to the pandemic shock. This leads the authors to identify the similarities and differences between two types of banks in the MENA region in a pandemic shock context.

Information Systems research on emotions in relation to using technology largely holds essentialist assumptions about emotions, focuses on negative emotions and treats technology as a token or as a black box, which hinders an in-depth understanding of distinctions in the emotional experience of using artificial intelligence (AI) technology in context. This research focuses on understanding employees' emotional experiences of using an AI chatbot as a specific type of AI system that learns from how it is used and is conversational, displaying a social presence to users. The research questions how and why employees experience emotions when using an AI chatbot, and how these emotions impact its use.

An interpretive case study approach and an inductive analysis were adopted for this study. Data were collected through interviews, documents review and observation of use.

The study found that employee appraisals of chatbots were influenced by the form and functional design of the AI chatbot technology and its organisational and social context, resulting in a wider repertoire of appraisals and multiple emotions. In addition to positive and negative emotions, users experienced connection emotions. The findings show that the existence of multiple emotions can encourage continued use of an AI chatbot.

This research extends information systems literature on emotions by focusing on the lived experiences of employees in their actual use of an AI chatbot, while considering its characteristics and its organisational and social context. The findings inform the emerging literature on AI.

Prior research has emphasised the importance of the early phases of construction projects, as well as the difficulties of procuring engineering services – especially due to the uncertainties. Despite that, studies on the public procurement of engineering services are scarce. Although scholars have shown that uncertainty may affect the choice of control modes, the level of uncertainty that characterises services is not addressed by the two task characteristics: knowledge of the transformation process and output measurability. The purpose is to investigate organisational control in public procurement of engineering services.

The existing control model was adjusted in this study by conceptually adding uncertainty as a third aspect to the two task characteristics. A single case study of the Swedish Transport Administration was used. The empirical data, comprising 14 interviews with managers from the client and engineering consulting companies, were analysed using flexible pattern matching and visual mapping approaches and then illustrated using the model.

The public client did not base its choice of control modes on uncertainty, but rather on the other two task characteristics. Consequently, the service providers argued that the chosen control modes reduced their creativity, increased their financial risks and caused unclear responsibilities. This study therefore shows that uncertainty is an important factor to consider in the choice of control modes, both from a theoretical perspective and from the service providers' point of view. The developed model may therefore be useful for researchers as well as practitioners.

This study is the first attempt to add uncertainty as a task characteristic when choosing control modes. The results contribute to the scarce control literature regarding the procurement of engineering services for construction projects and the procurement of other services with high uncertainty.

The purpose of this study is to investigate whether, in the context of making a go/no-go decision regarding a failing new product, the use of a stopping rule and/or a new decision-maker would reduce the escalation of commitment (EOC).

This study uses a classroom experiment design and uses logistic regression and a chi-square test to analyze its data.

The findings show that both responsible and non-responsible participants are more likely to perceive the negative performance of a new product as less negative and believe that the goal for the product can be reached when there is a stopping rule and proximal negative feedback indicates a level of performance below but very close to it than when there is no stopping rule. Therefore, they are more likely to continue the failing new product, whether they are responsible for the product or not. However, non-responsible decision-makers are more likely than their responsible counterparts to discontinue the failing new product in the absence of a stopping rule.

This paper extends the theory of EOC by showing that the use of a stopping rule and/or a new decision-maker may not reduce EOC.

This paper provides useful guidelines for managers on how to reduce EOC.

The originality and value of this paper are found in the investigation of a situation in which the use of a stopping rule and/or a new decision-maker may not reduce the EOC.

Literature on entrepreneurial finance has long overcome the view of an investor as a sole provider of financial capital. Entrepreneurs need to consider more aspects when deciding on an investor. Especially the depiction of corporate venture capital (CVC) investors has long highlighted advantages and disadvantages compared to independent VC (IVC) investors. The authors investigate what drives entrepreneurs' preferences for CVC relative to IVC and thereby focus on two key issues in the entrepreneur's consideration – the role of resource requirements and exit strategies.

The data were collected in an online survey that gathered information on several characteristics of entrepreneurs and their ventures. The resulting data set of 105 German entrepreneurs was analyzed using logistic regression and revealed important drivers for entrepreneurs' investor preferences.

The study’s findings confirm that the venture's resource needs, specifically the need for marketing resources and access to the corporate network, which play a significant role in the decision on whether a CVC or IVC investor is preferred. Moreover, the analysis debunks the hypothesis that entrepreneurs view a CVC investment as the first step toward acquisition. However, those entrepreneurs striving for an IPO are less likely to prefer CVC.

The study expands the literature on CVC attractiveness and specifically considers the entrepreneurs' intentions and needs. The results confirm but also debunk some widespread perceptions about why entrepreneurs choose to pursue financing from a CVC investor.