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Research on market information use in product innovation suggests that firms utilize two key strategic decision-making processes: incremental and comprehensive. Drawing from organizational information processing theory, literature implies that these processes operate differently. However, this assumption remains untested. Moreover, the degree to which a comprehensive process affects the innovation strategy outcomes depends on market information time sensitivity (MITS) and analyzability. To-date, no study has tested these assertions, either. Finally, it is suggested that meaningful market strategy is a key driver of new product success and it is important to understand how decision-making processes influence it under differing time sensitivity and analyzability.

Based on survey data from 250 Chinese firms, authors use structural equation modeling to test the hypotheses.

The results generally support authors’ contentions. More specifically, marketing strategy outcomes are influenced by marketing strategy incrementality (MSI) and marketing strategy comprehensiveness (MSC) differently. Further, time sensitivity moderates the effect of both MSI and MSC on outcomes, except for the effect of MSI on decision quality. Finally, analyzability moderates the relationships between decision making processes and certain strategy outcomes such as between MSI and meaningfulness.

Drawing from information processing theory, authors argue that incremental and comprehensive marketing strategy decision making for new product operate differentially under the same conditions. Further, the effects of these decision processes on outcomes depend on time sensitivity and analyzability of market information. Finally, auhtors argue that meaningful market strategy is a driver of success. The authors find support for most of our hypotheses and provide directions for future research.

Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.

Empirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.

First, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.

This paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.

This qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.

This paper describes how financial professionals' behavioral biases influence their financial forecast and decision-making process. Most of the earlier studies are focused on well-developed financial markets, and little is researched about financial professionals, such as institutional investors, portfolio managers, investment advisors, financial analysts, etc., in emerging markets.

An expert-validated questionnaire measure four prominent behavioral biases and Indian financial professionals' rational decision-making process. The final sample consists of 274 valid responses using the purposive sampling technique. IBM SPSS and AMOS structural equation modeling (SEM) software are used to build measurement and structural models, multivariate analysis including regression, factor analysis, etc.

The results provide empirical insights into the relationship between behavioral biases and the decision-making process. The results suggest that the structural path model closely fits the sample data. The presence of behavioral biases indicates that financial professionals' forecasting and decision-making is not always rational but bounded rational or irrational due to these factors. Furthermore, these biases (except overconfidence bias) have a markedly significant and positive relationship with irrational decision-making.

It is critical to eradicate these psychological errors, but awareness and attentiveness toward behavioral biases may help financial professionals to make informed decisions. Investors can improve their portfolio decisions and investments by recognizing their judgment errors and focusing on specific investment strategies to mitigate the impact of these biases. It is necessary to incorporate behavioral insights while developing training techniques for financial professionals. Rules of thumb, visual tools, financial coaching and implementing social-cultural elements in training programs enable financial professionals to develop simple, engaging, appealing and customized approaches for their clients.

This novel study is the first of this kind of research that examines the relationship between financial professionals' behavioral biases and rational decision-making process. This study significantly and remarkably provides insights into irrationality in financial professionals' decision-making.

This study aims to examine the international market selection process of entrepreneurs operating internationally.

Four small and medium-sized comparative and rich-information case studies were purposefully selected from among Australian and Arabian firms. Data were collected via in-depth personal interviews, follow-up interviews and questionnaire instrument.

The results revealed that entrepreneurs used a four-stage systematic decision-making process to attain profitable foreign market choices. The decision process was influenced by cognitive boundaries as entrepreneurs relied on the availability experiential, anchoring and adjustment heuristic.

The research’s findings and the proposed decision model will, significantly, assist entrepreneurs, willing to expand internationally, in enhancing their decision-making to attain profitable foreign market choices. Further, it provides benefits to foreign investment policymakers in host countries by assisting them to attract more inward foreign direct investments, and, accordingly, enhance the economic and social development movement in their countries.

This study provides a significant theoretical contribution to the literature on the internationalization process of entrepreneurs and small- and medium-sized enterprises through developing a decision model for selecting and entering foreign markets by entrepreneurs in a cross-country context. Further, the study provides significant methodological contributions with regard to the effectiveness of the qualitative case study method in capturing elements of the foreign market selection process.

The purpose of this paper is to study the crisis management process for project-based organizations (PBOs) by developing a comprehensive model and propositions.

This paper is based on a conceptual study. A literature review is considered a primary source for studying contemporary research, including 171 publications in total, which embody qualitative, quantitative, conceptual and theoretical studies. For data analysis, content analysis is used, which is comprised of descriptive and thematic analysis.

This study identifies five imperative elements of crisis management for PBOs which include (1) sense-making (information gathering and crisis interpretation), (2) decision-making (accurate and timely decision), (3) response (reactive response), (4) outcome (success/failure) and (5) learning. Based on these findings, this study proposes an integrative model of the interplay between sense-making, decision-making, response, outcome and learning. Furthermore, the findings lead to propositions for each of the elements. The paper contributes to the literature on dynamic capability theory.

This paper explores the crisis management process for PBOs. The proposed model deepens the understanding of the practices and processes of project-based crisis management.

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.

This paper aims to explore how the maieutic role of management accounting (MA) can be sustained in the context of MA digitalization.

The paper begins with practitioners’ descriptions of the context that makes the MA support of non-routine decisions maieutic. To understand how the maieutic characteristics can be sustained in future MA digitalization, the authors then analyze the discourses these practitioners have about artificial intelligence (AI) in providing MA support.

As a basis, the authors’ data show various maieutic characteristics within the use of MA answers in decision-making as well as within the MA process of generating such answers. The paper then identifies three MA digitalization discourses, namely, “computation,” “judgment” and human-AI “interaction” discourse, each with their unique agendas on how AI should be used.

The paper is based on the premises that AI and digitalization are often discussed without sufficient understanding about the context being digitalized. The authors’ data suggest that MA support in non-routine decision-making is fundamentally maieutic, and AI – as it currently stands – is not expected to change this by providing perfect answers. The authors provide novel insights about maieutic MA support and the current discourses on using AI in MA support, and how digitalization does not necessarily compromise maieutic MA support but instead has the potential to sustain or even enhance it.

This paper examines the relationship between marketing automation emergence and the marketers' use of heuristics in their decision-making processes. Heuristics play a role for the integration of human decision-making models and automation in augmentation processes, particularly in marketing where automation is widespread.

This study analyzes qualitative data about the impact of marketing automation on the scope of heuristics in decision-making models, and it is based on evidence collected from interviews with twenty-two experienced marketers.

Marketers make extensive use of heuristics to manage their tasks. While the adoption of new automatic marketing tools modify the task environment and field of use of traditional decision-making models, the adoption of heuristics rules with a different scope is essential to defining inputs, interpreting/evaluating outputs and control the marketing automation system.

The paper makes a contribution to research on the relationship between marketing automation and decision-making models. In particular, it proposes the results of in-depth interviews with senior decision makers to assess the impact of marketing automation on the scope of heuristics as decision-making models adopted by marketers.

Digitalization, innovation and changing customer requirements drive the continuous improvement of an organization's business processes. IT demand management (ITDM) as a methodology supports the holistic governance of IT and the corresponding business process change (BPC), by allocating resources to meet a company's requirements and strategic objectives. As ITDM decision-makers are not fully aware of how the as-is business processes operate and interact, making informed decisions that positively impact the to-be process is a key challenge.

In this paper, the authors address this challenge by developing a novel approach that integrates process mining and ITDM. To this end, the authors conduct an action research study where the researchers participated in the design, creation and evaluation of the approach. The proposed approach is illustrated using two sample demands of an insurance claims process. These demands are used to construct the artefact in multiple research circles and to validate the approach in practice. The authors applied learning and reflection methods for incrementally adjusting this study’s approach.

The study shows that the utilization of process mining activities during process changes on an operational level contributes to (1) increasing accuracy and efficiency of ITDM; (2) timely identification of potential risks and dependencies and (3) support of testing and acceptance of IT demands.

The implementation of this study’s approach improved ITDM practice. It appropriately addressed the information needs of decision-makers and unveiled the effects and consequences of process changes. Furthermore, providing a clearer picture of the process dependencies clarified the responsibilities and the interfaces at the intra- and inter-process level.

The purpose of this research is to analyze the influence of heuristics on Brazilian investors' behavior in the decision-making process.

The authors apply the partial least squares structural equation modeling methodology. This sample is composed of 220 investors.

The heuristics of overconfidence and anchoring positively influence investors' decision-making, while loss aversion negatively influences it. The herd effect exhibits no influence. The results also support the idea that decision-making positively influences investors' performance. Investors feel secure in their attitudes regarding financial decision-making, even if their decisions are not always rational as they are affected by biases.

This article explains the influence of heuristics on investors' decision-making and performance in the Brazilian context during the COVID-19 pandemic.