1,721,202 research outputs found
Digital data, dynamic capability and financial performance: an empirical investigation in the era of Big Data
Full text linkFirms automatically and continuously capture a high amount of digital data through social media, RFID tags, clickstreams, smart meters, manufacturing sensors, equipment logs, and vehicle tracking systems. However, empirical evidence on the effects of the generation of these digital data on firm performance remains scarce in the Information Systems and Management literature. Therefore, from a dynamic capability perspective, this paper examines whether companies’ ability to leverage digital data, which we call their Digital Data dynamic capability, leads to better financial performance, and whether there are moderating effects on this relationship. In order to achieve these goals, the following research questions are addressed: 1) To what extent do firms that develop Digital Data dynamic capabilities achieve better financial performance? 2) To what extent do organisational and industry-related environmental conditions moderate the relationship between a firm’s Digital Data dynamic capability and financial performance? We empirically test our hypotheses through partial least square modelling using a financial database and a survey of sales managers from 125 firms. We find that the development of Digital Data dynamic capability provides value in terms of firm financial performance and that the moderating effects are influential: under high levels of dynamism and munificence in younger firms, the relationship is stronger. Overall, this study evaluates the potential business value of firm digital data use and addresses a lack of empirical evidence on this issue in the Information Systems literature. We discuss two managerial implications. First, managers should pay more attention to digital data phenomena and to ways of leveraging value creation opportunities. Second, managers must evaluate their environmental and organisational characteristics when business opportunities from digital data are taken into account
Antecedents of Dynamic Capabilities and IT-dependent Initiatives in the Context of Digital Data
No full textThe Effect of Brand on the Impact of e-WOM on Hotels' Financial Performance
No full textTravellers increasingly consider electronic word of mouth (e-WOM) from online review platforms when making their accommodation decisions. Theory predicts that e-WOM increases sales, but little is known regarding the role that branding plays in the relation between e-WOM and hotels' financial performance. This study relied on financial data (including Revenue per Available Room - RevPAR - and sales profitability) and 34,164 online customer reviews gathered from TripAdvisor (as a proxy for e-WOM) based on panel data from 221 hotels, including not-branded and branded chain hotels in France from 2005 to 2013. The results show that the volume of reviews has no effect on RevPAR growth for branded chain hotels and a positive effect on RevPAR growth for not-branded chain hotels. Additionally, the direct effect of the valence of online reviews and its interaction effect with the yearly and cumulative volume of online reviews on RevPAR growth and sales profitability is shown to apply to not-branded chain hotels but not to branded chain hotels. This study contributes to the e-WOM literature by revealing the role of a brand in e-WOM's impact on hotel financial performance. Based on our results, managers of branded chain hotels should know that they might find it difficult to leverage e-WOM to achieve higher RevPAR growth and greater sales profitability, as their economic advantages derive from their brand instead of e-WOM
The Roles of the Antecedents in the emergence of a Dynamic Capability: the case of Born Digital Data capability
No full textInternational audienceExplaining variations in competitive advantage across organizations remains a persistent question for strategic management and organizations scholars. We empirically contribute to this explanation through an understanding of the different influences of the antecedents of dynamic capabilities (DCs), organizational processes, firm history, and firm assets, in the case of Born Digital Data (BDD) Capability. We define a BDD as the real-time inception in digital form of an informational representation of an entity state or event. BDD is an emerging phenomenon on which IT-dependent initiatives and DC can create new competitive opportunities. We contribute to the literature on DC antecedents in high-velocity markets by evaluating DC antecedents' influence on DC in IT-dependent initiatives in the case of BDD. Finally, we highlight the influence of BDD initiatives and BDD Capability on its output in terms of digital data accessibility. Our results show the different influences of the antecedents. The organizational processes of sensing, integrating, and coordinating the firm's history with its assets support BDD initiatives. The organizational processes of learning, sensing, coordinating, and integrating strengthen the BDD capability. Finally, BDD initiatives and BDD capability improve the accessibility of digital data
Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects
No full textInternational audienc
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Management of Big data: An empirical investigation of the Too-Much-of-a-Good-Thing effect in medium and large firms
No full textInternational audienceFirms adopt Big data solutions, but a body of evidence suggests that Big data in some cases may create more problems than benefits. We hypothesize that the problem may not be Big data in itself but rather too much of it. These kinds of effects echo the Too-Much-of-a-Good-Thing (TMGT) effect in the field of management. This theory also seems meaningful and applicable in management information systems. We contribute to assessments of the TMGT effect related to Big data by providing an answer to the following question: When does the extension of Big data lead to value erosion? We collected data from a sample of medium and large firms and established a set of regression models to test the relationship between Big data and value creation, considering firm size as a moderator. The data confirm the existence of both an inverted U-shaped curve and firm size moderation. These results extend the applicability of the TMGT effect theory and are useful for firms exploring investments in Big data.Les entreprises adoptent des solutions pour gérer les données massives, mais un ensemble de preuves suggère que, dans certains cas, les données massives pourraient créer plus de problèmes que d’avantages. Nous avançons l’hypothèse que le problème n’est peut-être pas les données massives en elles-mêmes mais une trop grande quantité de données massives. Nous apportons une réponse à la question de recherche suivante : quand les données massives conduisent-elles à la destruction de valeur ? Ce type d’effets fait écho à l’effet des excès des bonnes choses («Too-Much-of-a-Good-Thing» - TMGT) décrit en gestion. Cette théorie semble également significative et applicable en systèmes d’information. Nous contribuons à l’évaluation de cet effet TMGT lié aux données massives. Nous avons collecté des données auprès d’un échantillon d’entreprises, et nous avons établi un ensemble de modèles de régression pour tester la relation entre les données massives et la création de valeur, en considérant la taille de l’entreprise comme un modérateur. Les données confirment l’existence d’une courbe en forme de U inversé et l’existence d’une modération liée à la taille de l’entreprise. Ces résultats élargissent l’applicabilité de la théorie de l’effet TMGT et peuvent être utiles aux entreprises qui envisagent d’investir dans les données massives
Taxonomy for real-time digital data initiatives
Full text linkInternational audienceReal-time digital data are becoming important assets in a growing number of organizations. This paper, applying the affordance theory, describes the development of a taxonomy for understanding real-time digital data initiatives. The proposed taxonomy is composed by two categories, the Technology affordance and the Affordance actualization, respectively gathering four and five dimensions. Specifically, the Technology affordances of the real-time digital initiatives are real-time sensing, real-time mass visibility, real-time experimentation and real-time coordination, while the Affordance actualizations are service, efficiency, analytics, aggregation and generationLes données digitales en temps réel deviennent des atouts importants dans un nombre croissant d'organisations. Cet étude applique la théorie des affordances dans l'élaboration d'une taxonomie permettant de comprendre les initiatives en données digitales en temps réel. La taxonomie proposée est composée de deux catégories, les affordances technologiques et l'actualisation des affordances, regroupant respectivement quatre et cinq dimensions. Plus précisément, les affordances technologiques sont la détection en temps réel, la visibilité de masses en temps réel, les expérimentations en temps réel et la coordination en temps réel, tandis que les actualisations d'affordances concernent le service, l'efficacité, l'analyse, l'agrégation et la génération
Le succès des Systèmes d’Information de localisation des experts : le rôle de la Communauté de Connaissance
No full textIn the diversity of the Information Systems research, this thesis is mainly backed up by the reference disciplines of Information Systems and Management. The research topic is Knowledge Management, which is studied at the level of the individuals, who are considered members of Knowledge Communities. The author adopted a positivist research approach to this topic and applied the survey methodology as main research method.In our society, knowledge is considered, by individuals and by organizations, an economic resource and it surges as the only long-term sustainable competitive advantage. Nowadays, Information and Communication Technology (ICT) are giving chances to enhance the management of knowledge in the organizations containing its costs. In this attempt to contain costs, organizations are trying to train their members basing on the existing knowledge, because transferring existing knowledge is cheaper than creating new knowledge. Within this document, “knowledge transfer” refers to the communication of knowledge from an individual or an organization and its reception and application by another individual or organization.Knowledge involves cognitive structures and processes and it cannot be embodied in texts or other explicit representations. Even though knowledge transfer requires always human action, ICT can play an important role in the knowledge transfer, by the very beginning.Empirical results demonstrate that the ability to transfer knowledge positively contributes to the organizational performance of firms in both the manufacturing and service sectors. Although the benefits of the knowledge transfer have been documented in many settings, the effectiveness of this transfer varies considerably among the organizations. Moreover computer-based systems supporting the transfer of knowledge are less diffused and successful, justifying the research effort on this theme.The first step to knowledge transfer is the recognition of the heterogeneous distribution of knowledge among individuals. ICT supports this activity, but some significant steps could be done toward much more efficient solutions.Knowledge redundancy refers to the existence between the parties of common information, in addition to the specific information required immediately by each individual. This knowledge redundancy is assured by the participation to the same Knowledge Community, which is definable as a group of people who share a common practice, work, or interest. Whether there is knowledge redundancy among the sender and the potential recipient of knowledge, the recognition of the heterogeneous distribution of the knowledge among the individuals makes the knowledge transfer possible. The Knowledge Community has therefore a crucial role in knowledge transfer. Since previous research reports the central role of knowledge for competitive advantage, it is imperative for organizations to explore more effective solutions for levering this knowledge. This research study is proposed in an attempt to contribute in solving this lag, and under the hypothesis that Knowledge Communities and computer-based systems can facilitate the transfer of knowledge. In the research area where Knowledge Communities, computer-based systems and Knowledge Management overlap, this study focused on the computer-based systems that counsel the individuals that could be potential sources of specialized knowledge within a Knowledge Community. The author calls this type of computer-based systems “Expert Recommending” systems because they counsel the individuals who could likely help the users to solve problems of business process breakdowns.In this research, the author studies the Expert Recommending systems as a service. Instead of focusing on the computer-based system in it-self, the author is interested in the service it delivers, the Expert Recommending Service. Consistently with this service perspective, the research object would include also the cases in which this ERS is delivered without any computer-based support, thus by a specific department or by the members of the Knowledge Community by them-selves. Its specificity reposes on its functionality of supporting the individual awareness on the knowledge domains of the other individuals.The awareness regards the acknowledgement of the domains of Knowledge of the Others. Being aware of the individuals who could be source of specialized knowledge, i.e. knowing what the other members know, is a precursor to search a specific individual out, when some specialized knowledge is required.This study approaches the research object with three research questions: • What are the dimensions of the success of the Expert Recommending Services? • What are the properties of the Knowledge Community that influence the success of the Expert Recommending Services? • To what degree the success of the Expert Recommending Services is influenced by the properties of the Knowledge Community?They concern the Expert Recommending Service and the Knowledge Community, because this study assumes that an increase in the success of the ERS has a positive effect on the amount of the knowledge transfer. Nevertheless, the author considers the analysis of the knowledge transfer out of the research scope, limiting the research scope at the enhancement of the awareness in the knowledge distribution among the members.With these three research questions the author aims to contribute: 1. To describe the success of the Expert Recommending Services within Knowledge Communities. 2. To predict the degree of the success of the ERS within the KC, depending on the characteristics of the ERS and of the KC. 3. To identify recommendable interventions to enhance the success of the Expert Recommending Services within Knowledge Communities.The answers to the three research questions and the attainment of the aims of this research are obtained through the completion of a research process that includes a preliminary literature review and a subsequent empirical testing of the research model.The literature review started from the theory of the resource-based view of the firm. An evolution of this theory, the knowledge-based view of the firm gave the theoretical ground to the organizational knowledge management. Within the topic of knowledge management, the role of the Information Systems was analyzed. At the end the specific type of Information Systems, aiming at the enhancement of the knowledge awareness, the Expert Recommending Services, was explored.Subsequently, the research model and the research methodology were developed. The literature review backed up the design of the conceptual model that was employed in the empirical part of the research.The Information Systems Success theories and models were declined to the research object, the Expert Recommending Services in the Knowledge Communities, in order to build the specific conceptual model for this research.The conceptual model involved three main elements: 1. The Expert Recommending Service. 2. The Knowledge Community. 3. The success of the Expert Recommending Service.The model assumed the existence of two causal relations linking: 1. The Expert Recommending Service to the Success of the ERS. 2. The Knowledge Community to the success of the ERS.This conceptual model was converted into the empirical research model.Among the various IS success models, the choice of the one relied on its fitness to the research questions, aims, and context. The model that better matched these criteria was the DeLone and McLean’s IS Success Model, which was therefore taken as reference model.The methodological guidelines of Straub, Igalens and Roussel, and Evrard, Pras et al. were followed to promote the quality of the results.This research combined complementary qualitative and quantitative research methods to: • provide a richer contextual basis for interpreting and validating results, • compensate the weaknesses inherent in each single individual method, • grant a more precise development and investigation of the hypotheses, • favor the reliability and generalizability of the results.Multi-method research can assume different perspectives and the one followed in this study was the evolutionary perspective. The evolutionary perspective is particularly useful when little research has been conducted so far on a particular phenomenon, or where research hypotheses require increased focus. This was exactly the case of this study because little research in IS discipline was done and the hypothesized relationships between Knowledge Communities and ERS Success needed to be developed.Through an initial explorative study, qualitative data was gathered to interpret a wide range of topics in the area of investigation. The collected data was analyzed and the findings represented the basis for the development of the hypotheses for the following quantitative study.The definition of a first qualitative phase followed by a quantitative one has to be associated with the selection of the specific method for the qualitative study and the selection of the specific method for the quantitative study. Using the selection criteria proposed by Wood, the selected method for the exploratory phase is case study research. This choice has been mainly influenced by the cost and the potential for theory generation of case study research. The selected method for the confirmatory phase was opinion research for the cost and the potential of the opinion survey for the theory confirmation.The qualitative method is adopted to explore the characteristics of the Knowledge Communities, the characteristics of the Expert Recommending Services and the characteristics of the Success of the ERS and the potential relationships between them.The application of the selected IS success model to the context of the Expert Recommending Services leaded at the definition of two preliminary propositions: • P1: The characteristics of the Knowledge Community have an influence on the Success of the ERS. • P2: The characteristics of the Expert Recommending Service have an influence on the Success of the ERS.These propositions were explored through the qualitative method in order to establish precise hypotheses.In the qualitative phase the unit of analysis was the organization, with its ERS and its KC. This organization was studied through the analysis of the Knowledge Communities that exist in the organization, the understanding of the Expert Recommending Services that are provided in the organization, and the exploration of the relationship between Knowledge Communities and Expert Recommending Services.The case unit was analyzed through the collection of primary and secondary data. Primary data sources were interviews, direct observation, and informal discussions. Secondary data sources were mainly a set of documents of the organization that are normally produced by the organizational information system.A preliminary gathering of background information about the case foreran the collection of primary data and the main source of information was the internet web site of the organization. Supplementary, some internal secondary data was provided by the organizational referee.After this preliminary step, the names and the positions of all the potential participants were obtained, in collaboration with the internal referee. The potential participants were contacted for an interview and the collection of some complementary secondary data.The interviews were semi-structured interviews to different people of the selected organization, in order to cover the maximum heterogeneity of the interviewees and explore convergence of information from the different sources.The interview guide listed the main themes and sub-themes to discuss in the interview and was drafted beforehand to find out the view of the different individuals. At the beginning of each interview an introduction on the reasons and the objects of the interview was performed. This explanation was expected to reduce the researcher effects at the site, which biases the data collection.The interview guide was designed to learn what the individual’s view was on: the characteristics of the interviewee, the description of the ERS, the description of the Knowledge Communities in the organization, the opinion on the success of the ERS.The qualitative data produced by the interview survey was transcribed, following the convention proposed by Silverman. These transcriptions, the field notes on the direct observation and the collected secondary data were achieved in a repository.Each transcript was analyzed in parallel with the prosecution of the other interviews in order to use the content of the previous interviews as source of questions to ask in the next interviews. This continuous refinement influenced the composition of the interview guide and the deepness of the interviews on some specific aspects.For the data analysis, the author assumed that interview data was giving access to facts about the world. The author processed the content to explain the characteristics of the ERS, the characteristics of the Knowledge Communities and the success of the ERS. For the data analysis and interpretation, the author chose the thematic content analysis method, which is based on a system of themes and sub-themes. The premise of content analysis is that the repetition of units in speech (such as words, phrases, sentences or paragraphs) points out the centers of the interests and the opinions of the speakers. The sentences, the parts of the sentences or the groups of the sentences were grouped based on the relation to the themes of: Knowledge Communities, Expert Recommending Services and success of the ERS. As well as, the interview guide changed in the prosecution of the interviews also the list of themes and sub-themes was refined based on the relevance and interest of the different themes and sub-themes.Moreover, the closeness in time of the interviews and the analysis of their content gave the sensitivity on the saturation of the themes and the sub-themes. This closeness allowed the interruption of the scheduling of new interviews, as soon as the analysis revealed the saturation and repetition of the same themes.A computer aided qualitative data analysis system was employed to support codification and analysis. Several instruments were reviewed, direct and indirectly by Lewins, and the choice favored the use of HyperResearch package. The selection of this packaged software was based on its easiness of use and its flexibility in building reports.The quantitative method was adopted to confirm the results coming from the qualitative exploratory method. This confirmation aimed at measuring the relationships among the Knowledge Communities, the Expert Recommending Service and the Success of the ERS. The empirical research model was corroborated through the test of the hypotheses rising from the qualitative phase and the conceptual model.The application of the selected IS success model to the context of the Expert Recommending Services and the results of the qualitative phase leaded at the definition of the following constructs. • Perceived Usefulness to the Organization. It measures the effects of the ERS on the organizational performance in line with the proposal of DeLone and McLean with the variable Organizational Impact. • Perceived Usefulness to the Individual. It measures the effects of the ERS on the individual performance in line with the proposal of DeLone and McLean with the variable Individual Impact. • Use. It measures the utilization of the ERS by the individuals in line with the proposal of DeLone and McLean with the variable IS Use. • User Satisfaction. It measures the satisfaction of the user on the provision of the ERS that means on the answers obtained from the demands for counseling some experts. • ERS Quality. It measures the global judgment relating to the superiority of the ERS. • Knowledge of the Others. It measures the degree to which people know each other and in relation to the ERS context, Knowledge of the Others is specifically related to the Knowledge of the Others’ knowledge domains.The process and ecology concepts provided the theoretical base for developing the temporal and causal influences among the dimensions of the IS success to DeLone and McLean. So the hypotheses on the ERS Success were the following ones: • H1: Perceived Usefulness for the Individual affects Perceived Usefulness for the Organization. • H2: Use affects Perceived Usefulness for the Individual. • H3: User Satisfaction affects Perceived Usefulness for the Individual. • H4: Use affects User Satisfaction. • H5: ERS Quality affects User Satisfaction. • H6: ERS Quality affects Use.In addition, the grounding relevance of Knowledge of the Others for the informal ERS success determines the addition of three more hypotheses: • H7: Knowledge of the Others affects User Satisfaction. The degree of awareness on the knowledge domains of the members of the Knowledge Community could influence the satisfaction on the provision of the ERS. The individual who knows the knowledge domains of the other members could directly target the individuals who could provide a fully satisfying ERS. • H8: Knowledge of the Others affects ERS Quality. The Knowledge of the Others could influence the choice of the person, whom to ask the provision of the ERS. The persons who have an extensive Knowledge of the Others could question the individuals who are more likely able to provide a high quality ERS. • H9: Knowledge of the Others affects Use. The knowledge of the other knowledge domains could influence the use of the ERS. The complete Knowledge of the Others’ knowledge domains makes the use of the ERS superfluous, since the individual can directly target the right expert, with the required knowledge, without passing through the ERS. On the other hand, the complete absence of awareness on the knowledge domains of the other could restrain the use of the ERS, since the individual does not know whom to ask for the ERS provision.At this phase, the required data was too specific to have the possibility to find appropriate secondary data sources. Exclusively primary data were collected and the instrument employed to collect it was a questionnaire.The questionnaire was composed by the existing measures that the author evaluated as the most suitable to the research model. For each construct the existing scales were identified and then adjusted to the research object and to the context.The administration of the questionnaire was anticipated by its reviewed by several people. They suggested adjustments to the terminology, in order to improve the fitting of the questionnaire with the organizational context. The final version of the questionnaire was published on a web server, accessible by all the members.The answering to the questionnaire was promoted through an email that was sent to the targeted individuals. The targeted individuals were the organization members who performed the activities of recommending and searching experts. At the moment, the response rate per week decreased at zero, a recall by email was sent.The questionnaire was proposed via email but the answers were collected via a web form. In this way, the responses’ data was automatically stored in the database.Data was mainly analyzed through Structural Equation Modeling statistical technique but a preliminary analysis on the quality of data was performed before testing the structural model.The data analysis was performed following the validation guidelines written by Straub, Bourdeau, and Gefen. These guidelines proposed to assure: the content validity, the construct validity, the reliability, the manipulation validity, the statistical conclusion validity.The statistical data analysis was supported by packaged software and SPSS and Amos were selected, after that several packages were reviewed, directly and indirectly. The choice of these statistical packages resided in their partial integration and in the previous experience of the author on them.This combination of qualitative and quantitative methods allowed the triangulation of the data, which cross-validated the achieved results as these results, coming from different sources, converged and were congruent. The different sources were related to the different studies of cases, as a mean to overcome the problems involved in the study of a single case.The entire empirical research, i.e. the qualitative and the quantitative phases, was applied in different contexts following the specification for a multiple-case study proposed by Yin. The choice toward a multiple case study aimed at exploring the Expert Recommending Services, the Knowledge Communities and their relationships with the Success of the ERS, in contrasting situations. The author researched the theoretical replication, which meant that the same methodology was replicated in the tentative to find similarities and differences among the values of the independent and the dependent variables, and to find relations between the cases. So, few heterogeneous cases with contrasting characteristics were deliberately selected, instead of seeking a direct replication in similar cases. The multiple-case study strengthened the external validity of the findings since the findings, from the different cases, supported the hypotheses. This sampling method gave the freedom to change the number of cases, in the multiple case study, during the prosecution of the research. So, the sampling of the cases followed a reasoning that aimed at identifying cases with contrasting situations and was based on the previously described theoretical framework. The cases were selected tak
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