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34 results for “SMES”
Optimizing Artificial Intelligence (AI) Chatbot Customer Service in Small and Medium Enterprises (SMEs) in E-Marketplace
<p><span>The rise of advanced technologies, such as AI- driven chatbots, enables SMEs in e-marketplaces to provide responsive and efficient customer support, improve engagement, and streamline services. However, customers increasingly express concerns about AI-supported chatbot services, which affects their willingness to engage with these technologies. Consequently, this study aims to examine the factors<span> </span>that<span> </span>influence<span> </span>customers'<span> </span>behavioral<span> </span>intentions<span> </span>to<span> </span>use<span> </span>AI and their intentions for the continued use of AI-supported chatbots. Using a purposive sampling technique, the study collected data from 152 respondents through an online questionnaire. To analyze and predict the findings from the collected data, the study employed PLS-SEM as its statistical approach. The<span> </span>results<span> </span>indicate<span> </span>that<span> </span>information<span> </span>quality, system quality, and service quality significantly influence trust. Furthermore, service quality, perceived ease of use, confirmation of expectations, and perceived usefulness affect user satisfaction. Additionally, confirmation of expectations impacts perceived usefulness, and user satisfaction influences the behavioral intention to use AI chatbots. The findings also reveal that trust, user satisfaction, and perceived usefulness effectively enhance the intention to continue using AI-driven chatbots. However, information quality and system quality do not correlate with user satisfaction, and confirmation of expectations does not relate to<span> </span>user satisfaction. These findings contribute valuable insights to the existing literature on AI- driven chatbot services. Furthermore, stakeholders involved with AI-driven chatbot services for SMEs will gain an understanding<span> </span>of<span> </span>how<span> </span>to<span> </span>enhance<span> </span>user-friendly<span> </span>chatbot<span> </span><span>services.</span></span></p>
Social Media Marketing for SMEs: Leveraging Information Systems for Sales Performance
<p><span>Small and medium enterprises (SMEs) may directly interact with more clientele using social media marketing (SMM). To ensure accurate data and effective monitoring of progress, the field of SMM requires implementing a robust information system. This study examines the impact of information systems on consumer engagement in social media marketing for SMEs. This approach assesses the adoption of social media marketing using a single variable. This study employed purposive sampling to identify 129 SMEs for cross-sectional preliminary research. The preliminary research will take place around Jabodetabek in May and June of 2024, utilizing a Google Form survey. The author analyzed the data obtained from PLS-SEM using Smart PLS 3.0. This preliminary research computed Partial Least Squares (PLS) scores and performed bootstrapping. From a statistical standpoint, social media enhances the marketing effectiveness of SMEs in Indonesia. The market's efficiency and performance demonstrate the gain. This preliminary research consists of nine variables, such as, competitor pressure, affordable marketing cost, user generated content, belief in information, customer relationship, social media marketing adoption, SMEs’ marketing engagement, and sales performance, with only one hypothesis exhibiting a non-significant relationship.</span></p> <p><span>Keywords—<em>social media marketing, SMEs, information systems, marketing engagement</em></span></p>
Intention to Use Cybersecurity in SMEs: Improving SMEs' Performance
<p><span>This study prompted by observation where there are a considerable percentage of SMEs had not yet adopted cybersecurity, while witnessing a substantial number of cyberattacks being faced in SMEs. This study aims to explore the level of interest and motivation among Small-Medium Enterprises (SMEs) in adopting cybersecurity to reduce and prevent cyberattacks. This research model uses Structural Equation Model (SEM) and SmartPLS 4.0 as statistical tools. By using Purposive Sampling Method data was collected via an online questionnaire with 129 respondents from 26 April to 26 May 2024, consisting of people who play a part in SMEs that have not adopted cybersecurity. The proposed model has eight variables: Intention to Use Cybersecurity (INT), Observability (OBSR), Competitive Pressure (CP), Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Compatibility (COMP), Technological Readiness (TR), and Trialability (TRI). The result stated that six out of ten hypotheses are accepted, and the rest are rejected.</span></p>
Impact of debt and taxes on earnings persistence of the Portuguese SMEs
<p>Database for manuscript "Impact of debt and taxes on earnings persistence of the Portuguese SMEs"</p>
Analysis Customer Behavior of Mobile Food Online Delivery (MFOD) Applications for SMEs
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Exploring The Impact of Social Media Adoption to Small Medium Enterprises (SMEs) Performance
<p>Questionnaires and meta data</p>
Building Resilient Business For SMES: The Role of Financial Management Skill, Networking Capabilities, and Business Model Innovation
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Exploring The Impact of Social Media Adoption to Small Medium Enterprises (SMEs) Performance
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Implementation of the ASCEND Training for Supervisors in Dutch Small and Medium-sized Enterprises (SMEs)
ClinicalTrials.gov study NCT06989398. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Implementation of DWM in Dutch SMEs
ClinicalTrials.gov study NCT06979089. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
The Role of Adopting Social Media Gamification (SMG) in SMEs towards Increasing Customer Retention and Loyalty
<p>Questionnaires and meta data</p>
Multilevel Interventions for Mental Health in SMEs and Public Workplaces - H-Work EU Project Dataset
<p>This Dataset includes data collected as part of the H-Work project, at timepoints T1-T7. The Data is merged from multiple sites and timepoints, for more information see info file.</p>
Data set documentation of two surveys to identify and validate the critical success factors (CSFs) in the implementation of Lean Six Sigma (LSS) in Colombian manufacturing SMEs
<p>Documentation describing the data sets of two surveys conducted with the aim of identifying and validating critical success factors (CSFs) in the implementation of Lean Six Sigma (LSS) in manufacturing SMEs in Colombia is included.</p> <p>The first, at a general level and named <strong><em>E1</em></strong>, had the objective of identifying manufacturing SMEs in Colombia that have implemented Lean, Six Sigma or LSS. The second, of a specific level and named <strong><em>E2</em></strong>, was applied in the SMEs found in <strong><em>E1</em></strong> and had the objective of identifying and prioritizing the CSFs in the implementation of LSS and to assess their importance and practice using Likert-type scales. It also made it possible to identify the best-known and most used LSS tools and investigate the specific results of LSS implementation in SMEs.</p> <p>The questionnaire proposed by J. Antony et al. (2008) and adapted by Timans et al. (2012) for LSS was used. The consent to use is included in the documentation. The technical concept of four LSS experts in Colombia was requested to adapt expressions to the context.</p> <p>In the statistical validation phase of the identified CSFs, the estimation of a set of statistical criteria commonly used in psychometric research was made using statistical software SPSS® Version 26 from IBM®. The validation of the factorial structure of the complete model made up of the identified factors and their observable variables was done through a Confirmatory Factorial Analysis using the graphic software SPSS Amos® Version 26 also from IBM®.</p> <p>To build the graphic model the observable variables (statements in the questionnaire E2 of Section III) were represented by rectangles and identified with the letter P (question in Spanish) and with the number according to the order of appearance from 1 to 51 (Example, P1). The critical success factors (CSFs), which are the variables that are not directly measurable or latent, were represented with ovals and identified with the letter F for factor and with the number according to the order of appearance in the questionnaire E2 Section III from 1 through 13 (Example, F1)</p> <p>The following documentary support files are included:</p> <ul> <li><em>Dataset documentation.pdf</em> file</li> <li><em>Consent for the use of the E2 questionnaire.pdf</em> file</li> <li>Anonymized data from <strong><em>E1</em></strong> survey with 352 complete responses in the <em>E1_survey_dataset.exc</em> file.</li> <li>Anonymized data from <strong><em>E2</em></strong> survey with 44 complete responses in the <em>E2_survey_dataset.exc</em> file.</li> <li><strong><em>E1</em></strong> survey instrument with all survey items in the <em>E1_survey_instrument.p</em>df file.</li> <li><strong><em>E2</em></strong> survey instrument with all survey items in the <em>E2_survey_instrument.pdf</em> file.</li> <li>Factorial structure of the CSFs of the initial model in the <em>CFA_CSF </em>Amos file.</li> <li><em>CSFs</em> SPSS file.</li> </ul>
Interview Fragenkatalog: Unlocking AI-based Knowledge Management Potential for SMEs: Exploring Semantic Search Adoption
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