Advanced Customer Analytics: Strategic Value Through Integration of Relationship-Oriented Big Data

Translating environmental corporate social responsibility into environmental performance and competitive advantage: a moderated mediation model

Organizations are extending their corporate social responsibility (CSR) practices to environmental corporate social responsibility (ECSR) to cope with environmental challenges. Yet, the process of translating ECSR into positive organizational outcomes is underdeveloped in the literature. The literature often ignored key boundary conditions, leaving the underlying mechanism underdeveloped. This research makes up for the deficiency by investigating the impact of ECSR on environmental performance and competitive advantage through the mediation of green product innovation and by examining important boundary conditions in a moderated mediation model. We collected data from 151 manufacturing companies using judgmental and snowball sampling to ensure that the target organizations perform certain ECSR activities and analyzed it using partial least square structural equation modelling (PLS-SEM). The findings reveal that ECSR leads to competitive advantage (CA) and environmental performance (EP) through green product innovation, and this indirect path is moderated by organizational agility. The findings add substantial value to the literature by documenting the impact of ECSR on CA and EP with moderated mediation paths. The study also offers implications for policymakers and managers to amplify the impact of ECSR on favorable organizational outcomes.

Organizational agility enabled by big data analytics: information systems capabilities view

Purpose

The current study aims to structure the existing knowledge about organizational agility from the information systems (IS) capabilities view and synthesizes how agility is enabled by big data analytics (BDA).

Design/methodology/approach

This study performs a systematic literature review with the lens of IS capabilities view and provides an integrative framework that represents how BDA improves organizational agility through the mediation of IS capabilities.

Findings

This systematic literature review synthesizes what is known and identifies what remains to be further studied with a focus on the relationship between BDA competency and organizational agility, which contributes to academic performance in BDA and agility research communities.

Originality/value

Despite a growing body of literature on the relationship between BDA and agility, a consolidated and systematic understanding of how BDA can enable organizational agility is generally missing. Therefore, the current study addresses this gap by proposing an integrative framework that elucidates the processes in which BDA competency leads to agility through the mediation of IS capabilities.

Examining User Heterogeneity in Digital Experiments

Digital experiments are routinely used to test the value of a treatment relative to a status quo control setting — for instance, a new search relevance algorithm for a website or a new results layout for a mobile app. As digital experiments have become increasingly pervasive in organizations and a wide variety of research areas, their growth has prompted a new set of challenges for experimentation platforms. One challenge is that experiments often focus on the average treatment effect (ATE) without explicitly considering differences across major sub-groups — heterogeneous treatment effect (HTE). This is especially problematic because ATEs have decreased in many organizations as the more obvious benefits have already been realized. However, questions abound regarding the pervasiveness of user HTEs and how best to detect them. We propose a framework for detecting and analyzing user HTEs in digital experiments. Our framework combines an array of user characteristics with double machine learning. Analysis of 27 real-world experiments spanning 1.76 billion sessions and simulated data demonstrates the effectiveness of our detection method relative to existing techniques. We also find that transaction, demographic, engagement, satisfaction, and lifecycle characteristics exhibit statistically significant HTEs in 10% to 20% of our real-world experiments, underscoring the importance of considering user heterogeneity when analyzing experiment results, otherwise personalized features and experiences cannot happen, thus reducing effectiveness. In terms of the number of experiments and user sessions, we are not aware of any study that has examined user HTEs at this scale. Our findings have important implications for information retrieval, user modeling, platforms, and digital experience contexts, in which online experiments are often used to evaluate the effectiveness of design artifacts.

The future of data-driven relationship innovation in the microfinance industry

Data-driven innovation (DDI) initiatives by microfinance institutes have transformed the global poverty alleviation landscape. Despite the fact that relationship building is one of the primary goals of DDI initiatives in microfinance operations, there has been little research on the dimensions of relationship quality. This study examines how DDI initiatives recognize and incorporate relational dimensions in their service offerings to alleviate poverty. Drawing on a systematic literature review, thematic analysis and interviews with 20 microfinance managers, this research explores the relationship quality parameters that need to be leveraged. Grounded in the resource-based theory, the findings of this study confirm trust and commitment as two key relationship capabilities. The findings contribute to a better understanding of how microfinance institutes can use DDI to achieve sustainable competitive advantage.

Operationalizing Artificial Intelligence Enabled Customer Analytics Capability in Retailing

The value of customer analytics (CA) and artificial intelligence (AI) has been discussed separately at the forefront of research for business, marketing, and operations management. In spite of the strategic importance of CA and AI, there has been a paucity of research regarding the role of AI in operationalizing customer analytics (CA) capability. To address the gap, this study draws on a systematic literature review and thematic analysis for identifying the value-based CA capability antecedents that operationalize through AI in the context of retailing. The findings of this study extend the resource-based view (RBV)-capability theory in the spectrum of market orientation, and technology orientation to generate a better intelligence of CA capability in the retail context; while also providing theoretically grounded guidance to the practitioners. Hence, retail practitioners will likely be able to engage customers and enhance customer delight by incorporating CA capability dimensions, which is powered by AI.

Identifying the Big Shots—A Quantile-Matching Way in the Big Data Context

The prevalence of big data has raised significant epistemological concerns in information systems research. This study addresses two of them—the deflated p -value problem and the role of explanation and prediction. To address the deflated p -value problem, we propose a multivariate effect size method that uses the log-likelihood ratio test. This method measures the joint effect of all variables used to operationalize one factor, thus overcoming the drawback of the traditional effect size method (θ), which can only be applied at the single variable level. However, because factors can be operationalized as different numbers of variables, direct comparison of multivariate effect size is not possible. A quantile-matching method is proposed to address this issue. This method provides consistent comparison results with the classic quantile method. But it is more flexible and can be applied to scenarios where the quantile method fails. Furthermore, an absolute multivariate effect size statistic is developed to facilitate concluding without comparison. We have tested our method using three different datasets and have found that it can effectively differentiate factors with various effect sizes. We have also compared it with prediction analysis and found consistent results: explanatorily influential factors are usually also predictively influential in a large sample scenario.

Dynamic Differential Game Strategy of the Energy Big Data Ecosystem Considering Technological Innovation

This study discusses how to create strategic value through energy big data and how to promote stakeholder interaction mechanisms in the evolution of the energy big data ecosystem. We use differential game methods to study the interaction between one power grid enterprise (PG) and one technology supplier (TS) under three different cost-sharing contracts: without cost-sharing contract, cost-sharing contract, and two-way subsidy contract. The effectiveness of the dynamic equilibrium strategies under different contracts is verified via numerical simulations. The results show that under the centralized decision scenario, the technological innovation investment, the degree of technological advancement of PG and TS, and the total profit of the supply chain system are superior to the decentralized decision scenario. The extent of TS technology innovation investment depends on the share rate of PG. Technology innovation investment and the profits of energy big data service supply chain stakeholders will increase with the sensitivity coefficient of technological advancement. Compared with contracts without cost-sharing and with cost-sharing, the two-way subsidy contract can provide the Pareto optimal solution for the investment trajectory of technological innovation and long-term profits. Theoretically, this study reveals a new perspective in the research on the relationship between power grid enterprises and technology suppliers under dynamic technology innovation. In practice, this study facilitates power grid enterprises and technology suppliers to form a closer cooperative relationship in the energy big data ecosystem. More importantly, it is helpful for power grid enterprises to make optimal transaction decisions at different stages of energy big data ecosystem evolution.

The impact of business analytics capabilities on innovation, information quality, agility and firm performance: the moderating role of industry dynamism

Purpose

As businesses keep investing substantial resources in developing business analytics (BA) capabilities, it is unclear how the performance improvement transpires as BA affects performance in many different ways. This paper aims to analyze how BA capabilities affect firms’ agility through resources like information quality and innovative capacity considering industry dynamism and the resulting impact on firm performance.

Design/methodology/approach

This paper tested the research hypothesis using primary data collected from 192 companies operating in Bangladesh. The data were analyzed using partial least squares-based structural equation modeling.

Findings

The results indicate that BA capabilities improve business resources like information quality and innovative capacity, which, in turn, significantly impact a firm’s agility. This paper also found out that industry dynamism moderates the firms’ agility and, ultimately, firms’ performance.

Practical implications

The contribution of this work provides insight regarding the role of business analytics capabilities in increasing organizational agility and performance under the moderating effects of industry dynamism.

Originality/value

The present research is to the best of the authors’ knowledge among the first studies considering a firm’s agility to explore the impact of BA on a firm’s performance in a dynamic environment. While previous researchers discussed resources like information quality and innovative capability, current research theoretically argues that these items are a leveraging point in a BA context to increase firm agility.

A scholarly network of AI research with an information science focus: Global North and Global South perspectives

This paper primarily aims to provide a citation-based method for exploring the scholarly network of artificial intelligence (AI)-related research in the information science (IS) domain, especially from Global North (GN) and Global South (GS) perspectives. Three research objectives were addressed, namely (1) the publication patterns in the field, (2) the most influential articles and researched keywords in the field, and (3) the visualization of the scholarly network between GN and GS researchers between the years 2010 and 2020. On the basis of the PRISMA statement, longitudinal research data were retrieved from the Web of Science and analyzed. Thirty-two AI-related keywords were used to retrieve relevant quality articles. Finally, 149 articles accompanying the follow-up 8838 citing articles were identified as eligible sources. A co-citation network analysis was adopted to scientifically visualize the intellectual structure of AI research in GN and GS networks. The results revealed that the United States, Australia, and the United Kingdom are the most productive GN countries; by contrast, China and India are the most productive GS countries. Next, the 10 most frequently co-cited AI research articles in the IS domain were identified. Third, the scholarly networks of AI research in the GN and GS areas were visualized. Between 2010 and 2015, GN researchers in the IS domain focused on applied research involving intelligent systems (e.g., decision support systems); between 2016 and 2020, GS researchers focused on big data applications (e.g., geospatial big data research). Both GN and GS researchers focused on technology adoption research (e.g., AI-related products and services) throughout the investigated period. Overall, this paper reveals the intellectual structure of the scholarly network on AI research and several applications in the IS literature. The findings provide research-based evidence for expanding global AI research.

Deploying Big Data Enablers to Strengthen Supply Chain Resilience to Mitigate Sustainable Risks Based on Integrated HOQ-MCDM Framework

In the face of global competition, competitive enterprises should pursue sustainable development, and strengthen their supply chain resilience to cope with risks at any time. In addition, big data analysis has been successfully applied in a variety of fields. However, the method has not been applied to improve supply chain resilience in order to reduce sustainable supply chain risks. An approach for enhancing the capabilities of big data analytics must be developed to enhance supply chain resilience, and mitigate sustainable supply chain risks. In this study, a decision framework that integrates two-stage House of Quality and multicriteria decision-making was constructed. By applying this framework, enterprise decision-makers can identify big data analytics that improve supply chain resilience, and resilience indicators that reduce sustainable supply chain risks. A case study of one of China’s largest relay manufacturers is presented to demonstrate the practicability of the framework. The results showed that the key sustainable supply chain risks are risks regarding the IT infrastructure and information system efficiency, customer supply disruptions, transport disruptions, natural disasters, and government instability. To reduce risk in sustainable supply chains, enterprises must improve the key resilience indicators ‘financial capability’, ‘flexibility’, ‘corporate culture’, ‘information sharing’, and ‘robustness’. Moreover, to increase supply chain resilience, the following most important big data analysis enablers should be considered: ‘capital investment’, ‘building big data sharing mechanism and visualisation’, and ‘strengthening big data infrastructures to support platforms and systems’. This decision framework helps companies prioritise big data analysis enablers to mitigate sustainable supply chain risks in manufacturing organisations by strengthening supply chain resilience. The identified priorities will benefit companies that are using big data strategies and pursuing supply chain resilience initiatives. In addition, the results of this study show the direction of creating a fruitful combination of big data technologies and supply chain resilience to effectively mitigate sustainable risks. Despite the limited enterprise resources, management decision-makers can determine where big data analysis enablers can be most cost-effectively improved to promote risk resilience of sustainable supply chains; this ensures the efficient implementation of effective big data strategies.

Multi-criteria decision-making leveraged by text analytics and interviews with strategists

Strategic decision-making in organisations is a complex process affected by preferences, experiences, perspectives, and knowledge, which, in most cases, are ambiguous, contradictory, and represented in unstructured data. This paper develops a methodological framework to address strategic decision-making processes from a multi-criteria perspective, assisted by text analytics and interviews. The framework comprises five stages and 12 steps, and is empirically tested in a decision scenario involving a strategic focus for future analytics initiatives in order to stimulate value generation from analytics. The proposed framework enables the discovery, validation, and prioritisation of strategic patterns from relevant interview data. Among six decision alternatives discovered in the validation scenario, customer analytics was the strategic focus most relevant to future analytics initiatives. This article contributes to understanding and addressing complex decision-making processes and mixed research in organisations, through a multi-criteria perspective leveraged by a text-driven computational approach.

Two Decades of Research on Consumer Behaviour and Analytics

The present study is a systematic literature review that identifies the context of consumer behaviour and analytics in business to forecast the future of consumer behaviour with changing business trends through TCCM (theory, context, characteristics, method) guidelines. The authors identified that prior research used theories in different disciplines to explain the phenomenon in customer behaviour and analytics literature. When considering the theory, these phenomena often can be segregated based on the industry (e.g., marketing, advertising, sales, healthcare, human resource management, tourism), focusing on status-based mechanisms (e.g., cross-gaming predictive models), inertia-based mechanisms (e.g., theory of rational expectations and adaptive learning), or relationship-based mechanisms (e.g., theory of consumer engagement behaviour).

Industry 4.0 transition: a systematic literature review combining the absorptive capacity theory and the data–information–knowledge hierarchy

Purpose

The effect of the transition toward digital technologies on today’s businesses (i.e. Industry 4.0 transition) is becoming increasingly relevant, and the number of studies that have examined this phenomenon has grown rapidly. However, systematizing the existing findings is still a challenge, from both a theoretical and a managerial point of view. In such a setting, the knowledge management (KM) discipline can provide guidance to address such a gap. Indeed, the implementation of fundamental digital technologies is reshaping how firms manage knowledge. Thus, this study aims to critically review the existing literature on Industry 4.0 from a KM perspective.

Design/methodology/approach

First, the authors defined a structuring framework to highlight the role of Industry 4.0 transition along with absorptive capacity (ACAP) processes (acquisition, assimilation, transformation and exploitation), while specifying what is being managed, that is data, information and/or (actual) knowledge, according to the data-information-knowledge (DIK) hierarchy. The authors then followed the systematic literature review methodology, which involves the use of explicit criteria to select publications to review and outline the stages a process has to follow to provide a transparent and replicable review and to analyze the existing literature according to the theoretical framework. This procedure yielded a final list of 150 papers.

Findings

By providing a clear picture of what scholars have studied so far on Industry 4.0 transition, in terms of KM, this literature review highlights that among all the studied digital technologies, the big data analytics technology is the one that has been explored the most in each phase of the ACAP process. A constructive body of research has also emerged in recent years around the role played by the internet of things, especially to explain the acquisition of data. On the other hand, some digital technologies, such as cyber security and smart manufacturing, have largely remained unaddressed. An explanation of the role of these technologies has been provided, from a KM perspective, together with the business implications.

Originality/value

This study is one of the first attempts to revise the literature on Industry 4.0 transition from a KM perspective, and it proposes a novel framework to read existing studies and on which to base new ones. Furthermore, the synthesis makes two main contributions. First, it provides a clear picture of the different digital technologies that support the four ACAP phases in relation to the DIK hierarchy. Accordingly, these results can emphasize what the literature has looked at so far, as well as which digital technologies have gained the most attention and their impacts in terms of KM. Second, the synthesis provides prescriptive considerations on the development of future research avenues, according to the proposed research framework.

Artificial Intelligence-Driven Digital Technologies to the Implementation of the Sustainable Development Goals: A Perspective from Brazil and Portugal

Innovativeness is a characteristic of digital technologies (DT), and they have been assuming an important role in economic, social, and environmental dimensions. Therefore, DT are relevant contributors for sustainable development goal (SDG) achievements. This study aims to compare the preference for artificial intelligence-driven digital technologies (AI-Driven DT) to achieve SDGs in Brazil and Portugal. An independent sample t-test analysis and Levene test are performed to identify potential artificial intelligence-driven digital technologies (AI-Driven DT) as favorable facilitators for SDG achievements in Brazil and Portugal. Based on the findings, a broader analysis is provided, to (i) indicate potential favorable SDGs, (ii) discuss differences between the countries in AI-Driven DT preferences in each SDG, and (iii) make recommendations for potential technologies that could receive more attention and investments in both regions to make emergent digital technologies succeed, with a particular emphasis on cleaner production. The analysis is organized into three dimensions: economic, social, and environment. At the end, a closing discussion is provided about the key guidelines and prospects that could be adopted to keep a strong and positive shift of AI-Driven DT developments and applications towards fully supporting the attainment of the SDG of United Nations Organization (ONU) Agenda 2030.

Business value of cognitive analytics technology: a dynamic capabilities perspective

Purpose

Novel and emerging technologies such as cognitive analytics attract a lot of hype among academic researchers and practitioners. However, returns on investments in these technologies are often poor. So, identifying mechanisms through which cognitive analytics can add value to firms is a critical research gap. The purpose of this paper is to theorize how cognitive analytics technologies can enable the dynamic capabilities of sensing, seizing and reconfiguring for an organization.

Design/methodology/approach

This conceptual paper draws on the extant academic literature on cognitive analytics and related technologies, the business value of analytics and artificial intelligence and the dynamic capabilities perspective, to establish the role of cognitive analytics technologies in enabling the sensing, seizing and reconfiguring capabilities of an organization.

Findings

Through arguments grounded in existing conceptual and empirical academic literature, this paper develops propositions and a theoretical framework linking cognitive analytics technologies with organizations’ dynamic capabilities (sensing, seizing and reconfiguring).

Research limitations/implications

This paper has critical implications for both academic research and managerial practice. First, the authors develop a framework using the dynamic capabilities theoretical perspective to establish a novel pathway for the business value of cognitive analytics technology. Second, cognitive analytics is proposed as a novel antecedent of the dynamic organizational capabilities of sensing, seizing and reconfiguring.

Originality/value

To the best of the authors’ knowledge, this is the first paper to theorize how cognitive analytics technologies can enable dynamic organizational capabilities, and thus add business value to an organization.

The Phishing Funnel Model: A Design Artifact to Predict User Susceptibility to Phishing Websites

Phishing is a significant security concern for organizations, threatening employees and members of the public. Phishing threats against employees can lead to severe security incidents, whereas those against the public can undermine trust, satisfaction, and brand equity. At the root of the problem is the inability of Internet users to identify phishing attacks even when using anti-phishing tools. We propose the phishing funnel model (PFM), a framework for predicting user susceptibility to phishing websites. PFM incorporates user, threat, and tool-related factors to predict actions during four key stages of the phishing process: visit, browse, consider legitimate, and intention to transact. We evaluated the efficacy of PFM in a 12-month longitudinal field experiment in two organizations involving 1,278 employees and 49,373 phishing interactions. PFM significantly outperformed competing models in terms of its ability to predict user susceptibility to phishing attacks. A follow-up three-month field study revealed that employees using PFM were significantly less likely to interact with phishing threats relative to comparison models and baseline warnings. Results of a cost-benefit analysis suggest that interventions guided by PFM could reduce annual phishing-related costs by nearly $1,900 per employee relative to comparison prediction methods.

Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis

Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts' evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation.

Understanding business analytics continuance in agile information system development projects: an expectation-confirmation perspective

Purpose

This paper seeks to examine how expectations from business analytics (BA) by members of agile information systems development (ISD) teams affect their perceptions and continuous use of BA in ISD projects.

Design/methodology/approach

Data was collected from 153 respondents working in agile ISD projects and analysed using partial least squares structural equation modelling techniques (PLS-SEM).

Findings

Perceived usefulness and technological compatibility are the most salient factors that affect BA continuance intention in agile ISD projects. The proposed model explains 48.4% of the variance for BA continuance intention, 50.6% of the variance in satisfaction, 36.7% of the variance in perceived usefulness and 31.9% of the variance in technological compatibility.

Research limitations/implications

First, this study advances understanding of the factors that affect the continuous use of BA in agile ISD projects; second, it contextualizes the expectation-confirmation model by integrating technological compatibility in the context of agile ISD projects.

Originality/value

This is the first study to investigate BA continuance intention from an employee perspective in the context of agile ISD projects.

Leading digital transformation through an Agile Marketing Capability: the case of Spotahome

Scholars and practitioners both acknowledge that agility is crucial in leading a digital transformation effort. However, empirical and theoretical research on agile capabilities in an international digital marketing setting is severely lacking. Drawing on qualitative research design methods, this exploratory single-case study involving the international digital firm Spotahome provides an empirical and theoretical investigation of a new marketing capability we define as an Agile Marketing Capability. We identify its key theoretical dimensions and provide empirical guidelines to facilitate its implementation. This study has important implications for international marketing managers, suggesting what strategic actions are needed to deploy agile practices in their marketing strategies and processes from a practical perspective, in the context of an Agile Marketing Capability.

Exploring the influence of knowledge management process on corporate sustainable performance through green innovation

Purpose

Enhancing green innovation for corporate sustainability is one of the recent issues globally. Knowledge management has been determined as a core factor that hamstrings green innovation. The existing literature was limited to expose the importance of the knowledge management process for corporate sustainable performance. Thus, this paper aims to examine the role of the knowledge management process for corporate sustainable performance with the integration of green innovation and organizational agility following the resource-based view theory.

Design/methodology/approach

Cross-sectional design was used in this study. Data were gathered through convenience sampling from 475 respondents of multinational manufacturing corporations of Pakistan, analyzed by using structural equation modeling.

Findings

This study revealed that the knowledge management process and its all constructs (acquisition, dissemination and application) lead toward green innovation; further, green innovation influences corporate sustainable performance and its all constructs (environment, economic and social). Green innovation partially mediates the association between the knowledge management process and corporate sustainable performance. Besides, organizational agility has a positive effect on green innovation and corporate sustainable performance but was not found moderating these relations. The study educates that organizations investing in innovative technologies and adopting greener strategies are not only adequate for achieving sustainable performance, soft issues such as knowledge management and organizational agility but also important factors in the current knowledge base economy.

Originality/value

This study is an attempt to examine the previously undiscovered multi-dimensional relationships among the knowledge management process, green innovation, organizational agility and corporate sustainable performance. The presence of a positive correlation among these constructs was observed, proving the conceptual framework for this study.

Unpacking the impact of social media analytics on customer satisfaction: do external stakeholder characteristics matter?

Purpose

Underpinned by the lens of Contingency Theory (CT), the purpose of this paper is to empirically evaluate whether the impact of social media analytics (SMA) on customer satisfaction (CS) is contingent on the characteristics of different external stakeholders, including business partners (i.e. partner diversity), competitors (i.e. localised competition) and customers (i.e. customer engagement).

Design/methodology/approach

Using both subjective and objective measures from multiple sources, we collected primary data from 141 hotels operating in Greece and their archival data from TripAdvisor and the Hellenic Chamber of Hotels (HCH) database to test the hypothesised relationships. Data were analysed through structural equation modelling.

Findings

This study confirms the positive association between SMA and CS, but it remains subject to the varied characteristics of external stakeholders. We find that an increase in CS due to the implementation of SMA is more pronounced for firms that (1) adopt a selective distribution strategy where a limited number of business partners are chosen for collaboration or (2) operate in a highly competitive local environment. The results further indicate that high level of customer engagement amplifies the moderating effect of partner diversity (when it is low) and localised competition (when it is high) on the SMA–CS relationship.

Originality/value

The study provides novel insights for managers on the need to consider external stakeholder characteristics when implementing SMA to enhance firms' CS, and for researchers on the value of studying SMA implementation from the CT perspective.

The Role of Information Systems in the Sustainable Development of Enterprises: A Systematic Literature Network Analysis

Information Systems (IS) play an important role in improving the efficiency of firms’ operations and supply chains, which links to sustainability. Therefore, this study conducted a systematic literature network analysis to review 132 articles that discuss current trends in the IS discipline. Based on a citation network analysis, this study discovered three main research domains (sustainable competitive advantage, environmental sustainability, and sustainable online social communities), and two emerging research domains (the role of IS in developing countries and sustainable information infrastructures). Furthermore, a main path analysis was conducted to understand the knowledge structure of each research domain. This addresses how different trends are reflected in the IS literature related to improving firms’ competitive advantages and environmental sustainability. The results found that the sustainable competitiveness of enterprises is improved by the synergy between IS and other recourses within in the enterprises. Green IS initiatives not only solve the issues of environmental sustainability, but also enhance sustainable competitive advantage (i.e., stock price). As social media becomes the optimal enterprise communication channel, this study discusses the factors affecting sustainable online social community, such as structural dynamics (i.e., membership size, communication activity), social dynamics (the basic nature of interactions among members), participation costs, and topic consistency. Overall, the Information System literature is highly focused on three areas—economy, environment, and society, which supports Triple Bottom Line theory.

Data science for business: benefits, challenges and opportunities

Purpose

This paper aims to identify the benefits of data science (DS) for organizations, highlighting the challenges and opportunities related to developing this capability.

Design/methodology/approach

Initially, a literature review was performed. Later, empirical data were collected through a structured electronic interview answered by 211 informants, who are most experienced managers of medium and large organizations from different economic sectors, and data were submitted to content analysis.

Findings

The most frequently observed benefits are as follows: support for data analysis and insight generation with agility; creation of a data-driven culture; improvement of data quality; facilitating the understanding of the business environment, opportunity sensing; and organizational performance management. The most observed challenges are as follows: data-driven culture; DS training; allocation of investments in analytical technologies; and data governance and strategy.

Research limitations/implications

In addition, to mapping the state of the art on the subject, it contributes to the expansion of scientific knowledge through the identification and disclosure of 11 benefit indicators and 16 challenge indicators associated with analytical capabilities.

Practical implications

To transform data into information and add value to the business, organizations need to make efforts to enable executive mindset change, the formulation of strategies and governance mechanisms gave the renewal of workforce competencies and the allocation of investments in information technology.

Originality/value

A vast body of empirical evidence is gathered that consolidates different views on the benefits and challenges associated with DS for business.

Building Relationships with Customer 4.0 in the Era of Marketing 4.0: The Case Study of Innovative Enterprises in Poland

(1) Background: Contemporary enterprises are putting more and more emphasis on shaping lasting and effective relationships with clients. This is not an easy task, especially in the conditions of Marketing 4.0, which imposes on enterprises the need to holistically consider the needs as well as the skills and inventiveness of customers. It can be assumed that Clients 4.0 require both changes in the scope of business processes and shaping communication with their environment. That is why it is important today to skillfully and correctly determine the complexity of communication with Clients 4.0, as well as to determine the importance of communication with clients for the development of enterprises—especially those operating in innovative industries. (2) Methods: Empirical research was carried out on a sample of 100 innovative enterprises listed on the NewConnect market in Poland. The respondents were managers at various levels. Two indicators were constructed based on these assessments: Customer Relationships Importance Index (CRII) and Customer Communication Complexity Index (CCCI). (3) Results: It was determined that the complexity of communication of innovative enterprises with customers is at a high level. What is more, the importance of relationships with Customers 4.0 for the development of innovative enterprises is at a high level. It was also indicated that there is no correlation between the level of complexity of relationships with Customers 4.0 and the importance of these relationships for the development of innovative enterprises. (4) Conclusions: Based on the survey, it can be assumed that the relations with Clients 4.0 have the greatest share in shaping the development of innovative enterprises in the areas of minimizing the number of complaints, financial liquidity and efficiency of core business processes. It is also important that, in shaping the complexity of communication with Clients 4.0, enterprises mainly consider traditional phone calls and email account.