Search results

This paper discusses logistics service providers' (LSPs’) energy efficiency initiatives for sustainable development, both from an evolutionary perspective and based on a framework consisting of actions, processes (i.e. at the operations interface) and services (i.e. at the customer interface).

Following a qualitative research design, semi-structured interviews were conducted with sustainability managers at LSPs and the data were analysed via inductive coding. Based on the results and the literature, the authors developed a maturity model for LSPs' transitions to environmental sustainability.

LSPs' sustainable development occurs via operational processes, services at the customer interface, and actions that support those processes and services. Energy efficiency efforts are characterised by process depth that helps LSPs to align with their customers' energy efficiency improvement processes. While services related to energy efficiency connect LSPs and their customers, actions in support vary depending on the logistics activities in which LSPs participate.

Further research is needed to test and verify the maturity model and to clarify the interdependency of its three dimensions.

By categorising energy efficiency initiatives and proposing a maturity model for LSPs' sustainable development via energy efficiency, the authors have developed a tool for logistics actors to assess their progress towards improved sustainability.

The paper contributes to the literature by providing a three-pillar framework to understand the sustainability transitions of LSPs through energy efficiency. Developing a maturity model using this framework also contributes to the literature with an approach to assess sustainability advancement in the logistics industry.

This study investigated the contribution of artificial intelligence (AI) to the efficiency of customer service. This study contributes to services technological innovation in process management, a field not yet settled in the literature.

AI is a multidisciplinary field of research that has stood out for the technological dynamism provided to organizational products and processes. The study was carried out at an Analytical Intelligence Unit (AIU) of a Brazilian commercial bank that applies AI integrated with IBM's Watson system. The study used data content analysis, structured and supported by Atlas.ti software.

The notorious AI cognitive maturity evolution allowed 181 million interactions and 7.6 million attendances in 2020, improving services efficiency, with gains in agility, availability, accessibility, resoluteness, predictability and transshipment capacity. The chatbot service reduced the queues of call centers and relationship centers, allowing the human attendant to perform more complex attendances.

The main limitations of this study relate to the research cutout and its borders, such as the choice of participants and their areas of activity, and the choice of the unit of analysis.

The results indicated that attendance through the virtual assistant increased by more than a 1,000% from 2019 to 2020, demonstrating the bank was technologically ready to face the Covid-19 pandemic effects.

In line with the evolutionary theory of innovation, the authors concluded that technological scaling in AI allows exponential gains in customer service efficiency and business process management. They also conclude that the strategy for creating AIUs is successful, once it allows centralizing, structuring and coordinating AI projects in R&D cooperation, cognitive computing and analytics.

The objective of this work is to demonstrate the relationships between the two main processes of research and development (R&D) activities: the knowledge generation phase (KPP) and the knowledge commercialization, or transfer, phase (KCP), in a sector that is intensive in this type of activity, such as the pharmaceutical sector. In addition, within the framework of the general objective of this work, the authors propose two other objectives: (1) make advances in network efficiency measurement models, and (2) determine the factors associated with efficiency in the KPP and in the KCP in companies of the pharmaceutical sector in Spain.

A Network Data Envelopment Analysis (NDEA) model (Färe and Grosskopf, 2000) with categorical variables (Lee et al., 2020; Yeh and Chang, 2020) has been applied, and a sensitivity analysis of the obtained results has been performed through a DEA model of categorical variables, in accordance with the work of Banker and Morey (1986), to corroborate the results of the proposed model. The sample is made up of 77 companies in the pharmaceutical sector in Spain.

The results obtained point to a greater efficiency of pharmaceutical companies in the KPP, rather than in the KCP. Furthermore, the study finds that 1) alliances between companies have been the accelerating factors of efficiency in the KCP (but patents have slowed this down the most); 2) the quality of R&D and the number of R&D personnel are the factors that most affect efficiency in the KPP; and 3) the quality of R&D again, the benefits obtained and the position in the market are the factors that most affect efficiency in the KCP.

The authors have not found studies that show whether the efficiency obtained by R&D-intensive companies in the KPP phase is related to better results in terms of efficiency in the KCP phase. No papers have been found that analyse the role of alliances between R&D-intensive companies and patents, as agents that facilitate efficiency in the KCP phase, covering the gap in the research on both problems. Notwithstanding, this work opens up a research path which is related to the improvement of network efficiency models (since it includes categorical variables) and the assessment of the opinions of those who are responsible for R&D departments; it can be applied to decision-making on the aspects to improve efficiency in R&D-intensive companies.

With the recent proliferation of AI, organisations are transforming not only their organisational design but also the input and output operational processes of the hiring process. The purpose of this paper is to explore the organisational and operational dimensions resulting from the deployment of AI during talent acquisition process.

The authors conducted semi-structured interviews and meetings with human resources (HRs) professionals, recruiters and AI hiring platform providers in Sweden. Using an inductive data analysis rooted in the principles of grounded theory, the study uncovered four aggregate dimensions critical to understanding the role of AI in talent acquisition.

With insights from algorithmic management and ambidexterity theory, the study presents a comprehensive theoretical framework that highlights four aggregate dimensions describing AI’s transformative role in talent recruitment. The results provide a cautionary perspective, advising against an excessive emphasis on operational performance driven solely by algorithmic management.

The study is limited in scope and subject to several constraints. Firstly, the sample size and diversity are restricted, as the findings are based on a limited number of semi-structured interviews and meetings with HRs professionals, recruiters, and AI hiring platform providers. Secondly, the rapid evolution of AI technologies means that the study’s findings may quickly become outdated as new advancements and applications emerge.

The results provide managers with actionable information that can lead to more precise and strategic management practices, ultimately contributing to improved organizational performance and outcomes. Plus, enhancing their ability to make informed decisions, optimize processes and address challenges effectively.

The results signal both positive and negative impacts on employment opportunities. On the positive side, AI can streamline recruitment processes, making it easier for qualified candidates to be identified and hired quickly. However, AI systems can also perpetuate existing biases present in the data they are trained on, leading to unfair hiring practices where certain groups are systematically disadvantaged.

By examining the balance between transactional efficiency and relational engagement, the research addresses a crucial trade-off that organizations face when implementing AI in recruitment. The originality lies in its critique of the prevailing emphasis on e-recruiting.

The purpose of this paper is to investigate how new technology-based firms (NTBFs) overcome established notions of scale and scope through business model innovation, leveraging the value from digitalization.

The study adopts an exploratory research design, drawing on a novel data set of 50 publicly available interviews with vertical farming (VF) industry leaders and insiders who represent 36 different organizations from North America and Europe.

The study develops a framework showing how NTBFs leverage the value from digitalization via a paradoxical approach combining both efficiency and novelty value drivers.

The study extends current theorizing on the desirability of a combined business model approach leveraging both efficiency and novelty from digitalization. Furthermore, the study is among the first to investigate the unique and highly technological context of VF.

In recent years, the public sector has faced the challenge of digitalisation. This has significantly impacted the relationships between citizens and public organisations and, thus, it widely affects participatory processes, such as participatory budgeting (PB); in fact, digital tools (DTs) have emerged as a solution, increasing citizen engagement whilst improving efficiency, reducing costs and saving time. This contribution analyses PB in Rome, which is also implemented with DTs, seeking to understand how DTs impact citizens’ role in creating public value.

The study is based on a qualitative approach, precisely by analysing a descriptive and exploratory single case study of PB’s first adoption in Rome in 2019. The information is obtained from multiple sources and examined through document analysis.

In the Roman context, DTs in PB primarily facilitated cost-effective information sharing, offering citizens basic participation. Unfortunately, the potential for more interactive DTs was overlooked, failing to enhance citizen engagement in critical phases like deliberation, evaluation or monitoring. Therefore, the tools did not fully support citizens becoming co-creators of public value instead of just users in governance.

The novelty of this study lies in exploring the difference between the use of DTs that assist citizens/users in improving service quality and those that support citizens in creating a public and shared value. It ventures further to assess various tiers of participation, meditating on the digital elements that stimulate active engagement and value creation instead of simply expanding the participant pool or process efficiency.

The paper aims to disentangle the physiological credit risk from the credit risk coming from the inefficient screening and monitoring management process. The analysis is conducted on a sample of 338 Italian banks–56 joint-stock banks (SpA), 23 cooperative banks (Popolari) and 259 mutual banks (BCCs)–over the time period 2006–2017.

The authors use the maximum likelihood method to estimate the efficient frontier, as a set of best management credit practices, which minimises the credit risk defined on the basis of the level of loans granted, the technical structure of the loan portfolio (such as credit lines, mortgages, consumer loans and other technical loan categories) and the interest rate charges.

The empirical results show that the increase in non-performing loans (NPLs) is related both to the severe and protracted recession in Italy, which significantly reduced borrowers' capacity to service their debt, and to other factors, such as banks' lending monitoring policies with limited capacity to work-out defaulted loans.

The authors propose a new approach to the study of the performance of the credit process. With the stochastic frontier, the physiological credit risk, assumed by the bank according to its lending activity and management choices, is separated from the credit risk resulting from an inefficient management of the screening and monitoring process. In addition, the authors analyse the determinants of the excess of NPLs. This aspect is considered particularly original because the scientific contributions which consider the causes of NPLs have largely focused on the level of NPLs not considering the physiological part, linked to the structure of the bank's loan portfolio and its operational strategy and therefore not compressible and in any case not attributable to mismanagement or moral hazard.

Despite many efforts within organizations toward business process orientation (BPO), research on real-world experiences remains in its infancy. The purpose of this paper is to redress the existing knowledge gap by analyzing a Swedish public housing company that has made notable effects regarding BPO and to explore lessons learned from the BPO journey (from 1998 to 2013).

The point of departure is principles in the BPO foundation, principles of successful BPM and effects in empirically based literature. The reconstruction of the narrative case study describes milestones and critical junctions, as well as effects based on quantitative and qualitative data.

Effects in BPO are demonstrated in terms of higher customer satisfaction, increased innovative ability, improved operational performance, higher employee satisfaction and, as a result of these, increased profitability. Theoretical constructs with implications for the theory building on BPO are suggested in a three-layer management framework – with capabilities and abilities emerging from the case study used as an illustrative example.

Lessons are learned regarding critical practices related to advancement in BPO. A strategy-building process based on eight design propositions is suggested to define the pre-conditions for BPO in an organization.

This is the first longitudinal case study to provide a comprehensive view and detailed insights of a BPO journey and top management performance toward a business process-oriented organization. Practitioners and BPM community get valuable insights into how the temporality and the context shape the BPO maturity process in terms of new organizational structure and roles during the journey.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

In the transitional process of promoting market-oriented interest rate, China is confronted with an important theoretical and practical issue: how to avoid bank runs and realize the smooth operation of the financial system. The purpose of this paper is to construct a bank-run dynamic model by taking into account a market environment with the transmission of multiple rounds of noise information, a comprehensive consideration of depositors’ expectation of return on assets (or earning rate/yields of assets), the efficiency of information processing and dissemination, and the different motives for premature withdrawal.

The authors discussed the dynamic process of bank runs, furnished the ratio and number of each round of bank run, and characterized the corresponding dynamic equilibrium as well. Furthermore, the authors expanded the benchmark model by incorporating the deposit insurance system (DIS) to discuss the action mechanism of DIS overruns.

The results show that DIS implementation has two opposite effects: stabilized expectation and moral hazard, by virtue of its influence over the two types of premature withdrawal motives of depositors; the implementation effect of DIS rests with the dual-effect comparison, which is endogenous to the institutional environment.

The policy implications are as follows: while implementing DIS, it is necessary to establish and improve the corresponding institutional construction and supporting measures, to consolidate market discipline and improve the supervisory role of the bank’s internal governance mechanism, so as to reduce the potential moral hazards. The financial system reform shall be furthered and the processing and dissemination efficiency of information be elevated to prompt depositors to form stable withdrawal expectations, thereby enhancing the stabilizing effect of DIS.