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Artificial intelligence (AI) has started to receive attention in the field of digital entrepreneurship. However, few studies propose AI-based models aimed at assisting entrepreneurs in their day-to-day operations. In addition, extant models from the product design literature, while technically promising, fail to propose methods suitable for opportunity development with high level of uncertainty. This study develops and tests a predictive model that provides entrepreneurs with a digital infrastructure for automated testing. Such an approach aims at harnessing AI-based predictive technologies while keeping the ability to respond to the unexpected.

Based on effectuation theory, this study identifies an AI-based, predictive phase in the “build-measure-learn” loop of Lean startup. The predictive component, based on recommendation algorithm techniques, is integrated into a framework that considers both prediction (causal) and controlled (effectual) logics of action. The performance of the so-called active learning build-measure-predict-learn algorithm is evaluated on a data set collected from a case study.

The results show that the algorithm can predict the desirability level of newly implemented product design decisions (PDDs) in the context of a digital product. The main advantages, in addition to the prediction performance, are the ability to detect cases where predictions are likely to be less precise and an easy-to-assess indicator for product design desirability. The model is found to deal with uncertainty in a threefold way: epistemological expansion through accelerated data gathering, ontological reduction of uncertainty by revealing prior “unknown unknowns” and methodological scaffolding, as the framework accommodates both predictive (causal) and controlled (effectual) practices.

Research about using AI in entrepreneurship is still in a nascent stage. This paper can serve as a starting point for new research on predictive techniques and AI-based infrastructures aiming to support digital entrepreneurs in their day-to-day operations. This work can also encourage theoretical developments, building on effectuation and causation, to better understand Lean startup practices, especially when supported by digital infrastructures accelerating the entrepreneurial process.

Offices have always been complex social systems. With the growth of office automation they will have to be recognized as socio‐technical systems. The problems of prediction of consequences of change — in particular, technological change — in such systems are examined in some length and the limitations of causal analysis noted. A distinction between causal and hermeneutic modes of prediction is drawn. Hermeneutic modes of prediction are advocated as necessary in addition to predictions based on causal models in order to overcome the shortcomings of the latter. The results of a hermeneutic predictive exercise are reported which shed some light on the possible impact of future technological change in the office.

This paper aims to introduce a crowd-based method for theorizing. The purpose is not to achieve a scientific theory. On the contrary, the purpose is to achieve a model that may challenge current scientific theories or lead research in new phenomena.

This paper describes a case study of theorizing by using a crowd-based method. The first section of the paper introduces what do the authors know about crowdsourcing, crowd science and the aggregation of non-expert views. The second section details the case study. The third section analyses the aggregation. Finally, the fourth section elaborates the conclusions, limitations and future research.

This document answers to what extent the crowd-based method produces similar results to theories tested and published by experts.

From a theoretical perspective, this study provides evidence to support the research agenda associated with crowd science. The main limitation of this study is that the crowded research models and the expert research models are compared in terms of the graph. Nevertheless, some academics may argue that theory building is about an academic heritage.

This paper exemplifies how to obtain an expert-level research model by aggregating the views of non-experts.

This study is particularly important for institutions with limited access to costly databases, labs and researchers.

Previous research suggested that a collective of individuals may help to conduct all the stages of a research endeavour. Nevertheless, a formal method for theorizing based on the aggregation of non-expert views does not exist. This paper provides the method and evidence of its practical implications.

Partial least squares structural equation modeling (PLS-SEM) has become popular in the information systems (IS) field for modeling structural relationships between latent variables as measured by manifest variables. However, while researchers using PLS-SEM routinely stress the causal-predictive nature of their analyses, the model evaluation assessment relies exclusively on criteria designed to assess the path model's explanatory power. To take full advantage of the purpose of causal prediction in PLS-SEM, it is imperative for researchers to comprehend the efficacy of various quality criteria, such as traditional PLS-SEM criteria, model fit, PLSpredict, cross-validated predictive ability test (CVPAT) and model selection criteria.

A systematic review was conducted to understand empirical studies employing the use of the causal prediction criteria available for PLS-SEM in the database of Industrial Management and Data Systems (IMDS) and Management Information Systems Quarterly (MISQ). Furthermore, this study discusses the details of each of the procedures for the causal prediction criteria available for PLS-SEM, as well as how these criteria should be interpreted. While the focus of the paper is on demystifying the role of causal prediction modeling in PLS-SEM, the overarching aim is to compare the performance of different quality criteria and to select the appropriate causal-predictive model from a cohort of competing models in the IS field.

The study found that the traditional PLS-SEM criteria (goodness of fit (GoF) by Tenenhaus, R2 and Q2) and model fit have difficulty determining the appropriate causal-predictive model. In contrast, PLSpredict, CVPAT and model selection criteria (i.e. Bayesian information criterion (BIC), BIC weight, Geweke–Meese criterion (GM), GM weight, HQ and HQC) were found to outperform the traditional criteria in determining the appropriate causal-predictive model, because these criteria provided both in-sample and out-of-sample predictions in PLS-SEM.

This research substantiates the use of the PLSpredict, CVPAT and the model selection criteria (i.e. BIC, BIC weight, GM, GM weight, HQ and HQC). It provides IS researchers and practitioners with the knowledge they need to properly assess, report on and interpret PLS-SEM results when the goal is only causal prediction, thereby contributing to safeguarding the goal of using PLS-SEM in IS studies.

This essay raises a concern about the trajectory that new institutionalism has been following during the last decades, namely an emphasis on heterogeneity, change and agentic behavior instead of isomorphism and conformist behavior. This is a crucial issue from the perspective of the philosophy and methodology of science since a theory that admits both change and stability as a norm has less scientific weight then a theory that predicts a prevalence of passivity and isomorphism over change and strategic behavior. The former provides explanations and predictions while the latter does not.

The paper offers an analysis of the nature, characteristics, functions and boundaries of institutional theories in the spirit of philosophy and methodology of science literature.

The power of the former institutional theory developed by Meyer, Rowan, DiMaggio and Powell lies in its generalization, explanation and prediction of observable and unobservable phenomena: as a typical organizational theory that puts forward directional predictions, it explains and predicts the tendency for organizations to become more similar to each other over time and express less strategic and interest-driven behavior, conforming to ever-increasing institutional pressures. A theory of isomorphism makes scientific predictions while its modern advancements do not. Drawing on Popper's idea of the limit of domains of explanation and limited domains of theories we present two propositions that may direct our attention towards the strength or weakness of institutional theories with regard to their explanations of organizational processes and behavior.

The paper draws implications for further theory building in institutional analysis by suggesting the nature of institutional explanations and the place of institutional change in the theoretical apparatus. Once institutional theory explains the tendency of the system towards equilibrium, there is no need to explain the origins and causes of radical change per se. Institutional isomorphism theory explains and predicts how even after radical changes organizational fields will move towards isomorphism, that is, institutional equilibrium. The task is, therefore, not to explain agency and change but to show that it is natural and inevitable processes that organizational field will return to isomorphic dynamics and move towards homogenization no matter how much radical change occurred in this field.

The paper discusses the practical problems with instrumental utility of institutional theories. In order to be useful any theory must clearly delineate its boundaries and offer explanations and predictions and it is only the former 1977/1983 institutional theory that satisfies these requirements while modern advancements merely offer ambiguous theoretical umbrellas that escape empirical tests. For researchers therefore it is important to recognize which theory can be applied in a given limited domain of research and which one has little or no value.

Divergence in the development of East Asian and Latin American NICs is catching the attention of a growing number of political economists. This divergent development has sparked debates over THEORY between advocates of neo‐liberal and neo‐dependency approaches (Biersteker; Stallings: 370) in accounting for the regional divergence: does the East Asian success confirm modernization theory (neo‐liberalism) generally, or does each region require its own theory? (see Barrett and Whyte on Taiwan; Alschuler: chap. 4 and Lanzarotti: chap. 5 on Korea; Evans, 1987). East Asian “miracles” have led to equally bitter controversies over PRACTICE with regard to policy recommendations for third world nations: is the East Asian model exportable and is this desirable? (see Amsden; Fishlow; Broad and Cavanagh).

Much of the contemporary methodological literature tends to be self-referential and frequently ignorant of the breadth and depth of philosophical assumptions underpinning various methodological positions. Without a clear understanding of the philosophical underpinnings, logically deriving applicable validity criteria becomes very difficult (if not impossible). As a result, the purpose of this paper is to present a critical review of historical and more recent philosophical arguments for qualitative, quantitative, and mixed methods research in social science.

A targeted review of seminal philosophy of science papers dealing with ontological and epistemological assumptions of, and relation between, natural and social science.

The paper highlights the link between ontological/epistemological assumptions and methodological choices in social science. Key differences between the natural and social science are discussed and situated within the main paradigms.

The paper draws attention to a range of difficulties associated with the adoption of the natural sciences and the related positivist approaches as a role model for work in the social sciences. Unique contributions of interpretive and critical approaches are highlighted. The paper may be of value to scholars who are interested in the historical context of the still-ongoing qualitative-quantitative debate.

The purpose of this paper is the study of the causal relationship. The concept called “naive” causality can be stated more generally as the belief (or knowledge) that results follow actions, and that these results are not random, but are consistently linked with causes. The authors have thus formed a very general and precarious concept of causality, but one that appropriately reflects the meaning of causality at the level of common sense.

Mathematical and logical development of the causality in complex systems.

There are three aspects of rationality that give the human mind a unique vision of reality: quantification: reduction of phenomena to quantitative terms; cause and effect: causal relationship, which allows predicting; and the necessary and valid use of (deterministic) mechanical models. This work is dedicated to the second aspect, that of causality, but at present leaves aside the discussion of possibility-necessity, proposing a modification to philosophical synthesis of causality specified by Bunge (1959), with contributions made by Patten et al. (1976) and LeShan and Margenau (1982).

Causality is an epistemological category, because it concerns the experience and knowledge of the human subject, without being necessarily a property of reality.

In order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.

Based on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.

The results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R² = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.

The research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.

The paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.

There are two distinct types of forecasting model in which past experience is used as an indicator of the future, and these may be termed “causal” models and “non‐causal” models. (An extensive discussion of these model types appears in Robinson and Wood and Fildes.) Non‐causal (naive) models simply extrapolate past history on the forecast variable and disregard those forces which caused the particular pattern for the time series. The object is to select the type of curve which provides the closest fit to a given historical series, and complex statistical procedures exist for carrying out this exercise. The great problem with forecasting by extrapolation is that it presupposes that the factors which were the main cause of growth in the past will continue to be the main cause in the future, which may be incorrect, and if this is the case the use of this technique will result in poor forecasts. If one considers sales of foreign holidays as an example, one realises that there may be significant changes in the variables affecting these sales, such as income changes, fare changes, and changes in exchange rates. In order to forecast sales of foreign holidays reasonably accurately it is therefore necessary to construct a causal model in which sales are explicitly related to the determining forces.