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This study aims to investigate the impact of environmental scanning on organizational resilience through the mediation of organizational learning and innovation based on organizational information processing theory (OIPT) within Egyptian small and medium enterprises (SMEs) during the COVID-19 pandemic.

This study adopted a cross-sectional design to collect the data used to carry out mediation analysis. A self-administered questionnaire was used to collect data from a sample consisting of 249 Egyptian SMEs. The smart partial least square structural equation modeling (PLS-SEM) technique was adopted to test the hypotheses.

Environmental scanning does not have a direct effect on organizational resilience. However, organizational learning and innovation fully mediate the relationship between environmental scanning and organizational resilience.

The sample size was small, covering only Egyptian manufacturing SMEs. The results may differ in the service sector and other countries. The study was cross-sectional which is limited to tracing the long-term effects of environmental scanning, organizational learning and innovation on organizational resilience. Accordingly, a longitudinal study may be undertaken.

Managers in Egyptian SMEs should use signals from environmental scanning activities as input for learning and transforming business processes through innovation to develop organizational resilience.

This study is the first to investigate the role of environmental scanning in building organizational resilience through organizational learning and innovation based on the perspective of OIPT within Egyptian SMEs during the COVID-19 crisis.

This paper empirically examines the process of acquiring technology from a source, external to the firm, and incorporating it into a new product or operational process under development. We refer to this key activity in product and process innovation as external technology integration. This paper develops a conceptual model of external technology integration based on organizational information processing theory and a wide range of technology management literature. Field interviews were conducted to evaluate the validity of the model across diverse settings. Our results indicate general support for the conceptual model. We close with a discussion of the implications of this study for both theory and practice.

Humans tend to rely on beliefs, assumptions and cognitive rules‐of‐thumb for making judgments and are biased against taking more uncertain alternatives. Such inertia has implications for client organizations' decision making about innovations, which are inherently more uncertain than conventional alternatives. The purpose of this paper is to contribute to furthering the understanding of barriers to overcoming inertia in client decision making in new‐build.

A descriptive behavioural decision‐making perspective is combined with an organizational information‐processing perspective. To identify and discuss individual and organizational barriers that potentially distort clients' decision making on innovation, the analysis addresses aggregated data from four studies. The analysis focuses on inferences and interpretations made by decision makers in Swedish client organizations, their information‐processing practices and the subsequent impacts on perceived meanings and judgments about industrialized multi‐storey, timber‐framed building innovations, which are perceived by Swedish clients as new and different building alternatives.

Cognitive and organizational barriers maintain status‐quo decisions. Clients are inclined to make biased judgments about industrialized‐building alternatives because non‐applicable cognitive rules‐of‐thumb, based on their experiences of conventional‐building alternatives, are used. Furthermore, client organizations' information‐processing practices do not allow different meanings to surface, interact and potentially suggest different conclusions, at odds with established beliefs.

The paper's conclusions highlight how inertia is sustained in client decision making in new‐build. They illustrate the limitations of a common engineering approach, i.e. supporting decision making about innovations by focusing on providing more information to the decision maker in order to reduce uncertainty, as well as managing multiple meanings by reductionism.

Technological uncertainty and technological complexity are key characteristics of new product development (NPD) projects that impose significant information processing requirements on organizations. This paper examines the direct influence of technological uncertainty and technological complexity as well as the indirect influence of work experience on organizational information processing capabilities.

The author used a sample of 166 respondents from the German automotive industry and applied linear hierarchical regression analysis.

The results confirm a negative influence of technological uncertainty and technological complexity on organizational information processing capability. This research also supports a moderating influence of work experience on these relationships.

This research helps to understand the relationship between technological uncertainty, technological complexity and OIPC. It represents a first and different approach to measure these constructs for further empirical studies and provides interesting managerial implications.

The purpose of this paper is to investigate the relationship between the environmental uncertainty faced by public administrations and their likelihood of disclosing performance information, particularly at municipal level.

The existence of the relationship between environmental uncertainty and performance information disclosure is explored, drawing on organizational information processing theory. The paper describes an empirical quantitative investigation in a sample of 490 Italian municipalities.

Municipalities facing more uncertainty are more likely to disclose performance information. There is no unique set of factors that can explain the difference in the disclosure activity of Italian municipalities, but this activity appears to be contingent on the level of environmental uncertainty.

The paper explores the under-investigated field of factors influencing the disclosure of performance information by public administrations. It identifies uncertainty as one of the determinants of performance information disclosure. The findings suggest that the use of theories and variables not previously used in this type of study can improve understanding of the phenomenon. The study also suggests that public officials should consider adequate enforcement mechanisms to promote performance information disclosure, especially for organizations with lower incentives to improve information processing capabilities.

This study conceptualizes the digital transformation (DT) strategy in a supply chain context, identifies its drivers from intra- and inter-organizational perspectives and examines the effect of the DT strategy on the strategic agility and financial performance of Chinese manufacturing firms.

The authors constructed a theoretical model by synthesizing the diffusion of innovation and organizational information processing theory (OIPT) and provided a set of hypotheses. The authors empirically tested the arguments using partial least squares structural equation modeling using data from a sample of 200 manufacturing firms in China.

The findings indicate that while supply chain connectivity positively affects DT adoption and DT routinization, data analytics capability and organizational learning positively influence DT adoption but not DT routinization. The mediation analysis also shows that DT strategy has significant direct effects on financial performance and a stronger indirect influence on financial performance via improved strategic agility.

This study responds to repeated calls for a new understanding of supply chain DT strategy. In addition, the study offers important contributions to the literature by identifying the potential discord between the existing DT strategy and the supply chain context and proposes a new framework that provides essential theoretical underpinnings.

This study enriches the literature by conceptualizing and validating the dimensions, driving factors and performance implications of DT strategy in strategic supply chain management.

The global business environment combined with increasing societal expectations of sustainable business practices challenges firms with a host of emerging risk factors. As such, firms seek to increase supply chain transparency, enabling them to monitor operational activities and manage supply chain risks. Drawing on organizational information processing theory, the purpose of this paper is to examine how supply chain analytics (SCA) capabilities support operational supply chain transparency.

Using data from 477 survey participants, hypotheses are tested using seemingly unrelated regression.

The results reveal that: analytics capability in support of planning functions indirectly affects organizational supply chain transparency (OSCT) via SCA capabilities in source, make, and deliver functions; SCA capabilities in source, make, and deliver positively influence OSCT; and supply uncertainty moderates the relationship between SCA capabilities in make and OSCT.

This research suffers from limitations inherent in all survey-based research. Nonetheless, the authors found convincing evidence that suggests firms can employ SCA capabilities to meet transparency requirements.

The findings inform design of SCA systems, noting the importance of linking planning tools with tools that support source, make, and deliver functions. The research also shows how transparency can be increased via employing SCA capabilities.

This is one of first studies to empirically demonstrate that SCA capabilities can be used to increase supply chain transparency. The research also advances organizational information processing theory by illustrating an analytics capability paradox, where increased levels of certain analytics capabilities can become counterproductive in the face of supplier uncertainty.

In the digital age, enterprise social media (ESM) use is becoming more prevalent in the workplace. The “group” function is a very important part in the use of ESM. This paper explores how the characteristics of employees' task requirements affect their group participation behaviors on the ESM.

Based on information processing theory, the authors establish a two-stage research model to explore the impact of task characteristics on employees' online group participation behavior in the context of ESM. Data were collected using a survey of 341 Chinese employees.

The results indicate that (1) task interdependence was positively correlated with participation in small closed groups; (2) task complexity was positively correlated with participation in small groups, large closed groups and open professional groups and (3) task non-routineness was positively correlated with participation in small groups, large closed groups and open professional groups.

This study builds on the literature on task characteristics, information processing theory and employees' online group participation behavior, contributing to the research on ESM in the field of information systems and providing guidance for enterprise practice.

The purpose of this study is to develop a synthesized conceptual framework for artificial intelligence (AI) adoption in the field of business-to-business (B2B) marketing.

A conceptual development approach has been adopted, based on a content analysis of 59 papers in peer-reviewed academic journals, to identify drivers, barriers, practices and consequences of AI adoption in B2B marketing. Based on these analyses and findings, a conceptual model is developed.

This paper identifies the following two key drivers of AI adoption: the shortcomings of current marketing activities and the external pressure imposed by informatization. Seven outcomes are identified, namely, efficiency improvements, accuracy improvements, better decision-making, customer relationship improvements, sales increases, cost reductions and risk reductions. Based on information processing theory and organizational learning theory (OLT), an integrated conceptual framework is developed to explain the relationship between each construct of AI adoption in B2B marketing.

This study is the first conceptual paper that synthesizes drivers, barriers and outcomes of AI adoption in B2B marketing. The conceptual model derived from the combination of information processing theory and OLT provides a comprehensive framework for future work and opens avenues of research on this topic. This paper contributes to both AI literature and B2B literature.

Supply chain resilience (SCR) has attracted much attention in the context of the high uncertainty caused by the coronavirus disease 2019 (COVID-19), local regional conflicts and natural disasters. Based on information processing theory (IPT), this study investigates the role of supply chain information processing capability in enhancing SCR through supply chain governance (SCG), under different conditions of environmental uncertainty.

The hypothetical model is tested by using hierarchical regression on the primary samples collected from the Chinese manufacturing industry.

The results indicate that supply chain information processing capability has a significant positive effect on SCR. Also, SCG plays a mediating role between supply chain information processing capability and SCR. Furthermore, environmental uncertainty positively moderates the effect of supply chain information acquisition and supply chain information analysis on relational governance. However, environmental uncertainty only positively moderates the effect of supply chain information analysis on contractual governance.

This is the first study to explain the effect of information processing capability on SCR from the supply chain perspective, while also exploring the mediating role of SCG between SCR and supply chain information processing capacity, based on IPT.