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This research aims to investigate, in the context of a crisis, how adaptive marketing and open marketing capabilities directly contribute to enhance SMEs' business model innovation taking into consideration the mediating role of strategic flexibility.

Based on a sample of 120 small firms, our theoretical model is tested through a cross-sectional study. PLS-SEM is applied as the analytical technique.

The results show that open marketing capabilities are positively related to business model innovation and that this relationship is partially mediated by strategic flexibility. Furthermore, adaptive market experimentation capabilities enhance business model innovation only when fully mediated by strategic flexibility.

Extending existing explorative research, our research illuminates how adaptive market experimentation and open marketing capabilities, in conjunction with strategic flexibility, can help SMEs to better adapt existing business models during a time of crisis. Our findings underline the potential contribution of planned test-driven activities, trial-and-error processes, data-based decisional processes and benchmarking activities. We also document how stronger networking capabilities and organizational openness strengthen the firm's ability to access the required additional resources and insights they need. These contributions remain however conditioned by the use of a convenient sampling design as well as the cross-sectional nature of the data.

Our findings underline the importance of empowering SMEs to nurture more effective experimental approaches in the long run, along with a more formalized open marketing posture. Our study also highlights the need for SMEs to improve their awareness of the risk of inertia and the benefits of nurturing their overall flexibility so they can adapt in an adequate and timely manner.

The findings of this study build on the perspective of adaptive marketing capabilities and add to the business model innovation literature in two ways. First, our study provides new insights into the cumulative and concrete consequences of market experimentation and open marketing capabilities on small firms' business model dynamics in the context of a crisis. Second, our findings illuminate the crucial role of strategic flexibility which, partly or entirely, contributes to the full realization of the potential of the marketing capabilities at hand.

Credible influencers play a key role in shaping the views and preferences of social media users. However, many influencers intentionally use disinformation (e.g. false narratives) to deceive users and gain their trust. This can have serious repercussions, not only for the firms that associate with these influencers but also for users. Further, and alarmingly, many influencers who use disinformation can sustain their credibility over time. This research explores how influencers use disinformation to establish long-term credibility on social media.

Drawing on self-presentation theory, we use an in-depth qualitative case study to address our research question, primarily relying on archival data obtained from multiple sources.

Our findings suggest that three stages of self-presentation are required to establish influencer credibility based on disinformation: backstage (preparing to deceive), experimentation (testing deception), and frontstage (launching deceptive ideas on a large scale). We also find that when fraudulent influencers simultaneously weaponise a counterculture and mindfully encase disinformation, users view them as highly credible.

We offer practical suggestions for regulating fraudulent influencers, including enacting fact-checking procedures, using IT artefacts as reliability signals, and building awareness programmes to develop vigilance in social media communities.

We contribute to self-presentation theory by adding experimentation as a critical stage in developing disinformation that works for long periods. We also contribute to the literature at the intersection of social media influencers and disinformation research by revealing why social media users believe in fraudulent influencers.

Small- and medium-sized enterprises (SMEs) increasingly implement digitalization in uncertain business environments. However, a dearth exists in the entrepreneurship literature for understanding the decision-making logic of digitalization as a management issue. Drawing on the effectuation theory, this study aims to explore the relationships between effectuation dimensions and SMEs’ digitalization.

Using quantitative data collected from 345 Chinese SMEs through questionnaires, the authors conducted the principal component analysis and hierarchical linear regression analysis.

The results highlight significant positive relationships between the four effectuation elements – experimentation, affordable loss, flexibility and precommitment – and SMEs’ digitalization. Moreover, this research considers the environmental conditions as moderators and reveals that environmental dynamism and complexity associated with high uncertainty negatively moderate the effects of effectuation on SMEs’ digitalization.

SMEs embarking on digitalization should constantly experiment to determine optimal strategies while contemplating their affordable losses. Flexibility should also be maintained to discard unproductive tactics and redirect to other viable options. Additionally, precommitments can reduce the risk that SMEs encounter in digitalization process. While the effectuation principles consolidate the likelihood of a successful digitalization, this research recommends that entrepreneurs should carefully consider their possible application in uncertain environments.

This study contributes to the entrepreneurship literature by theoretically clarifying the decision-making mechanism of digitalization and extends the application of effectuation to this context by illuminating the influences of effectuation principles on SMEs’ digital transformation. The identification of negative moderating effects of environmental uncertainty also augments an academic criticism about uncertainty creating the conditions for effectuation.

The aim of this study is to enhance diversity, equity, and inclusiveness (DEI) culture by using individuals’ learning engagement (ILE) as a catalyst with formal and informal learning engagement interventions for sustainable learning and performance.

As part of a transdisciplinary study (DEI and learning), a mixed method was used. The data from 16 semi-structured interviews (SSI) with corporate professionals was analysed to create the ILE framework for enhancing DEI culture. A focus group discussion and experimentation were used for validation as triangulation.

The suggested ILE framework had a considerable impact on DEI. Findings revealed a positive, statistically significant association between ILE and DEI cultural enhancement describing how participants' ILE experiences impacted their DEI perspectives, demonstrating compelling evidence of DEI culture enhancement.

This study proposes a sustainable learning approach for a multi-cultural workforce by using the ILE framework for business, HR, DEI and L&D experts to enhance DEI culture.

The study contributes to DEI and learning engagement domains with a framework for integrating all three elements of DEI by using impactful ILE interventions.

Referred data set produces reliable information about the network flows and common attacks meeting with real-world criteria. Accordingly, this study aims to focus on the use of imbalanced intrusion detection benchmark knowledge discovery in database (KDD) data set. KDD data set is most preferably used by many researchers for experimentation and analysis. The proposed algorithm improvised random forest classification with error tuning factors (IRFCETF) deals with experimentation on KDD data set and evaluates the performance of a complete set of network traffic features through IRFCETF.

In the current era of applications, the attention of researchers is immersed by a diverse number of existing time applications that deals with imbalanced data classification (ImDC). Real-time application areas, artificial intelligence (AI), Industrial Internet of Things (IIoT), etc. are dealing ImDC undergo with diverted classification performance due to skewed data distribution (SkDD). There are numerous application areas that deal with SkDD. Many of the data applications in AI and IIoT face the diverted data classification rate in SkDD. In recent advancements, there is an exponential expansion in the volume of computer network data and related application developments. Intrusion detection is one of the demanding applications of ImDC. The proposed study focusses on imbalanced intrusion benchmark data set, KDD data set and other benchmark data set with the proposed IRFCETF approach. IRFCETF justifies the enriched classification performance on imbalanced data set over the existing approach. The purpose of this work is to review imbalanced data applications in numerous application areas including AI and IIoT and tuning the performance with respect to principal component analysis. This study also focusses on the out-of-bag error performance-tuning factor.

Experimental results on KDD data set shows that proposed algorithm gives enriched performance. For referred intrusion detection data set, IRFCETF classification accuracy is 99.57% and error rate is 0.43%.

This research work extended for further improvements in classification techniques with multiple correspondence analysis (MCA); hierarchical MCA can be focussed with the use of classification models for wide range of skewed data sets.

The metrics enhancement is measurable and helpful in dealing with intrusion detection systems–related imbalanced applications in current application domains such as security, AI and IIoT digitization. Analytical results show improvised metrics of the proposed approach than other traditional machine learning algorithms. Thus, error-tuning parameter creates a measurable impact on classification accuracy is justified with the proposed IRFCETF.

Proposed algorithm is useful in numerous IIoT applications such as health care, machinery automation etc.

This research work addressed classification metric enhancement approach IRFCETF. The proposed method yields a test set categorization for each case with error reduction mechanism.

This paper aims to (1) unveil how artificial intelligence (AI) can be implemented in growth-hacking strategies; and (2) identify the challenges and enabling factors associated with AI’s implementation in these strategies.

The empirical study is based on two distinct groups of analysis units. Firstly, it involves 11 companies (identified as F1 to F11 in Table 1) that employ growth-hacking principles and use AI to support their decision-making and operations. Secondly, interviews were conducted with four businesses and entrepreneurs providing consultancy services in growth and digital strategies. This approach allowed us to gain a broader view of the phenomenon. Data analysis was performed using the Gioia methodology.

The study firstly uncovers the principal benefits and applications of AI in growth hacking, such as enhanced data analysis and user behaviour insights, sales augmentation, traffic and revenue forecasting, campaign development and optimization, and customer service enhancement through chatbots. Secondly, it reveals the challenges and catalysts in AI-driven growth hacking, highlighting the crucial roles of experimentation, creativity and data collection.

This research represents the inaugural scientific investigation into AI’s role in growth-hacking strategies. It uncovers both the challenges and facilitators of AI implementation in this domain. Practically, it offers detailed insights into the operationalization of AI across various phases and aspects of growth hacking, including product-market fit, user acquisition, virality and retention.

Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.

As part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.

The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).

To the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.

The purpose of this paper is to propose and validate a robust industrial control system. The aim is to design a Multivariable Proportional Integral controller that accommodates multiple responses while considering the process's control and noise parameters. In addition, this paper intended to develop a multidisciplinary approach by combining computational science, control engineering and statistical methodologies to ensure a resilient process with the best use of available resources.

Taguchi's robust design methodology and multi-response optimisation approaches are adopted to meet the research aims. Two-Input-Two-Output transfer function model of the distillation column system is investigated. In designing the control system, the Steady State Gain Matrix and process factors such as time constant (t) and time delay (?) are also used. The unique methodology is implemented and validated using the pilot plant's distillation column. To determine the robustness of the proposed control system, a simulation study, statistical analysis and real-time experimentation are conducted. In addition, the outcomes are compared to different control algorithms.

Research indicates that integral control parameters (K_i) affect outputs substantially more than proportional control parameters (K_p). The results of this paper show that control and noise parameters must be considered to make the control system robust. In addition, Taguchi's approach, in conjunction with multi-response optimisation, ensures robust controller design with optimal use of resources. Eventually, this research shows that the best outcomes for all the performance indices are achieved when Kp11 = 1.6859, Kp12 = −2.061, Kp21 = 3.1846, Kp22 = −1.2176, Ki11 = 1.0628, Ki12 = −1.2989, Ki21 = 2.454 and Ki22 = −0.7676.

This paper provides a step-by-step strategy for designing and validating a multi-response control system that accommodates controllable and uncontrollable parameters (noise parameters). The methodology can be used in any industrial Multi-Input-Multi-Output system to ensure process robustness. In addition, this paper proposes a multidisciplinary approach to industrial controller design that academics and industry can refine and improve.

In recent years, the acronym VUCA has gained traction in strategic communication (SC) as an umbrella term that summarizes the recurrent challenges (volatility, uncertainty, complexity and ambiguity) of digital communication environments. However, an integrated reflection on how the VUCA dimensions facilitate a deeper understanding of specific digitalization challenges and how to navigate through these challenges is lacking. This article aims to explore and substantiate the descriptive (how) and prescriptive (how to) potential of VUCA for SC under digitalization conditions.

We first provide a systematic discussion of the four VUCA dimensions based on the general strategy literature. While their descriptive value is undisputed, prescriptive advice on how to respond to these challenges is contradictory. We substantiate this observation in a second empirical step based on problem-centered interviews with strategic communicators at the agency and corporate levels.

Our findings reveal that VUCA facilitates a systematic mapping of digitalization challenges consistently identified by professionals. The proposed strategic responses, however, remain contradictory at the theoretical and empirical levels. Hence, we propose the VUCA radar as a comprehensive descriptive and prescriptive framework.

The radar provides (a) a systematic overview of recurrent digitalization challenges to SC at the industry and practice levels and (b) prescriptive advice on how to navigate through these challenges by balancing contradictory strategic responses at the levels of vision, understanding, clarity and agility.

This study aims to investigate the evolving concept of the metaverse and its implications for service innovation. It seeks to understand how integrating technologies such as extended reality, blockchain, artificial intelligence and non-fungible tokens enables companies to experiment and innovate.

Adopting a qualitative methodology, this investigation conducts an immersive netnography across more than 25 case studies spanning diverse industries such as gaming, retail, health care and education. The thematic analysis method is used to distill critical insights, providing a deep dive into the technological enablers, innovation processes and market adaptations within the metaverse.

The study addresses four main building blocks through which companies experiment with the metaverse to foster innovation: enabling virtual identities’ agency, developing non-fungible tokens, designing immersive paths and crafting phygital microworlds. They shape the metaverse by enacting actors, resources, processes and phygital ecosystems. Companies obtain learning outcomes from such experimentation and identify learning challenges.

This research contributes to the nascent body of knowledge on the metaverse and service innovation by providing a comprehensive framework that encapsulates the multifaceted ways companies experiment within the metaverse. It extends the understanding of digital-physical convergence in service research, offering theoretical and practical insights into the development of phygital service ecosystems.

Este estudio tiene como objetivo investigar el concepto en evolución del Metaverso y sus implicaciones para la innovación en servicios. Busca comprender cómo la integración de tecnologías como la realidad extendida, la cadena de bloques, la inteligencia artificial y los tokens no fungibles permite a las empresas experimentar e innovar.

Adoptando una metodología cualitativa, esta investigación realiza una netnografía inmersiva a través de más de 25 estudios de casos que abarcan diversas industrias como los juegos, el comercio minorista, la atención médica y la educación. Se emplea el método de análisis temático para destilar conocimientos críticos, brindando un profundo análisis de los habilitadores tecnológicos, los procesos de innovación y las adaptaciones al mercado dentro del Metaverso.

El estudio aborda cuatro bloques principales a través de los cuales las empresas experimentan con el Metaverso para fomentar la innovación: habilitar la agencia de identidades virtuales, desarrollar tokens no fungibles, diseñar senderos inmersivos y crear micromundos físico-digitales. Estos dan forma al Metaverso mediante la actuación de actores, recursos, procesos y ecosistemas físico-digitales. Las empresas obtienen resultados de aprendizaje de dicha experimentación e identifican desafíos de aprendizaje.

Esta investigación contribuye al cuerpo de conocimiento incipiente sobre el Metaverso y la innovación en servicios al proporcionar un marco integral que encapsula las diversas formas en que las empresas experimentan dentro del Metaverso. Amplía la comprensión de la convergencia digital-física en la investigación de servicios, ofreciendo ideas teóricas y prácticas sobre el desarrollo de ecosistemas de servicios físico-digitales.

这项研究旨在探讨元宇宙概念的演变以及其对服务创新的影响。其目标在于深入理解通过整合增强现实、区块链、人工智能以及非同质化代币等技术, 如何使企业得以进行实验和创新。

本研究利用定性研究方法, 进行了一项沉浸式网络民族志调查, 涵盖了跨越游戏、零售、医疗保健和教育等多个行业的25多个案例。通过主题分析方法, 提炼出关键见解, 深入探讨了元宇宙内的技术驱动因素、创新过程和市场适应性。

这项研究以四个主要方面为切入点, 探讨了企业在元宇宙中促进创新的方法：启用虚拟身份的代理、开发非同质化代币、设计沉浸式路径以及打造物理-数字微观世界。通过这些措施, 企业塑造了元宇宙, 涉及行动者、资源、过程和物理-数字生态系统的执行。在此类实验中, 企业积累了丰富的学习经验, 并面临了

本研究旨在建立一个全面的框架, 以拓展关于元宇宙和服务创新的新知识, 揭示企业在元宇宙中进行实验的多方面方式。它不仅扩展了服务研究领域中数字与物理融合的理解, 还提供了有关发展物理-数字服务生态系统的理论和实践见解。